Compression and decompression of downlink channel estimates

ABSTRACT

A network node determines parameters indicating a compression function for compressing downlink channel estimates, and a decompression function. The network node transmits the parameters, receives compressed downlink channel estimates, and decompresses the compressed downlink channel estimates using the decompression function. A terminal device receives the parameters, forms the compression function, compresses downlink channel estimates using the compression function, and transmits the compressed downlink channel estimates. The compression function comprises a first function formed based on at least some of the parameters, a second function which is non-linear, and a quantizer. The first function is configured to receive input data, and to reduce a dimension of the input data. The decompression function comprises a first function configured to receive input data and provide output data in a higher dimensional space than the input data, and a second function which is non-linear.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/435,178 filed 31 Aug. 2021, which is a National Phase Application ofPCT/SE2019/050196 filed 6 Mar. 2019. The entire contents of eachaforementioned application is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to compression anddecompression of downlink channel estimates.

BACKGROUND

Antenna Arrays

All nodes in a 3GPP LTE/5G network can be equipped with arrays ofantenna elements to transmit and receive radio signals. FIG. 1illustrates a uniform linear array (ULA) with 8 co-polarized antennaelements. The 8 co-polarized antenna elements in FIG. 1 are spaced by xwavelengths λ. FIG. 2 illustrates a uniform planar array (UPA) with dualpolarized antenna elements regularly arranged into 4 rows and 8 columns.The polarizations are depicted in FIG. 2 using solid and dottedelements. It is now common for LTE deployments to have 4, 8, 16 or evenmore antenna elements at the base station (BS).

3GPP standards refer to antenna ports, instead of antenna elements. A3GPP antenna port is a logical entity and is defined such “that thechannel over which a symbol on the antenna port is conveyed can beinferred from the channel over which another symbol on the same antennaport is conveyed”. Essentially, an antenna port is defined by thereference signal transmitted from the antenna port, where the referencesignal can be mapped to the antenna elements (precoded) in an arbitraryfashion. For instance, DMRS antenna ports are precoded in the same wayas PDSCH symbols and are typically mapped to the entire antenna whileCSI-RS antenna ports are typically either mapped to individual antennaelements or subarrays or are alternatively also precoded in the similarfashion as PDSCH. It is common practice to partition an antenna arrayinto subarrays of physical antenna elements, where only a singletransmit-receive unit (TXRU) is connected to each subarray. That is,each antenna element of the subarray is fed the same input signal butwith a fixed phase and or amplitude offset. Thus, depending on context,a 3GPP antenna port can be a single physical antenna element, a subarrayof physical antenna elements or a general linear combination of theantenna elements of the array. The new radio (NR) release 15 standardsupports up to 32 digitally-precoded CSI-RS antenna ports. The number ofantenna elements is expected to grow significantly in future evolutionsof 3GPP 5G networks, particularly at the BS.

One of 3GPP's key strategies to improve system performance (e.g.,spectral efficiency) in 5G networks is to enable BSs to efficiently uselarge antenna arrays (e.g., as a single array or multiple panels ofsmaller arrays).

Let

-   -   N_(TX):=number of antenna ports at the BS    -   N_(RX):=number of antenna ports at the UE

The mapping from antenna ports to antenna elements is called an antennavirtualization.

Downlink Channel Model

The input to each antenna port is a sequence of complex-valuedmodulation symbols (e.g., QPSK, 16QAM, 64QAM or 256QAM). Orthogonalfrequency division multiplexing (OFDM) is used in LTE/5G networks toencode these symbols onto many orthogonal subcarriers for transmission.

The propagation channel connecting a network node (such as a BS) withsome terminal device (such as a user equipment (UE)) describes how theOFDM symbols transmitted from the BS's antenna ports are distorted bythe surrounding environment (buildings, trees, vehicles etc.) andreceived by the UE's antenna ports. The channel experienced by an OFDMresource element in LTE/5G networks is well-modeled byy=Hx+z.Here

-   -   y is an (N_(RX)×1) complex vector representing the signals        measured on the UE's antenna ports.    -   x is an (N_(TX)×1) complex vector representing the signals sent        from the BS's antenna ports.    -   H is an (N_(RX)×N_(TX)) complex matrix representing the channel        for a given OFDM resource element connecting the BS's antenna        array with the UE's antenna array. The (i,j)-th element of H,        which we denote by h_(ij), models the complex gain of the signal        path between the j-th antenna port at the BS and the i-th        antenna port at the UE.    -   z is an (N_(RX)×1) complex vector representing the representing        noise in the UE's receiver radio-frequency chain.

The channel matrix H for a given OFDM resource element (RE) encapsulatesmany unknown or unmeasurable variables, so it is appropriate to model itas random. An OFDM channel model for a deployment scenario, in thecontext of this invention, is a model for the joint distribution of theOFDM channel matrices over REs, OFDM symbols, and UEs. For example, thechannel modeling work in 3GPP TR 38.901 V15.0.0 and 3GPP TR 38.900V15.0.0 aim to provide realistic channel models for the following basicdeployment scenarios:

-   -   Urban micro scenario: Models outdoor-to-outdoor and        outdoor-to-indoor communications links, assuming the BSs are        mounted below surrounding rooftops. Example, the typical open        area is 50 to 100 m; the BS height is 10 m; the UE height is 1.5        to 2 meters; and the inter-site distance is 200 m.    -   Urban macro scenario: Models outdoor-to-outdoor and        outdoor-to-indoor communications links, assuming the BSs are        mounted above surrounding rooftops. Example, the BS height is 25        m; the UE height is 1.5 to 2 meters; and the inter-site distance        is 500 m.    -   Indoor scenario: Models typical indoor deployment scenarios,        including office environments and shopping malls. Example: The        BSs are mounted at a height of 2-3 m either on the ceilings or        walls. The shopping malls are often 1-5 stories high and may        include an open area (or “atrium”) shared by several floors.    -   Backhaul scenario: Models outdoor above roof top backhaul in an        urban area, where small cells are placed on lamp posts.    -   D2D/V2V scenario: Models device-to-device and vehicle-to-vehicle        deployments.

Other important factors that are often explicitly modeled are thecarrier frequency, the OFDM subcarrier spacing, the antenna/deviceconfiguration at the UE and BS (e.g., number of panels, panel spacing,number of elements within each panel and layout), and the devicemobility (e.g., walking or driving).

No single channel model can accurately model the OFDM channel matricesfor all real-world deployments. The OFDM channels observed in areal-world deployment are generated by a unique combination of, forexample, the physical surroundings, radio equipment, and devicemobility.

Active Antenna Systems and CSI at the BS

Active antenna systems are a key technology in modern LTE networks, andthey will become more important in future 5G networks. For example, akey differentiator between LTE and 5G networks will be the number ofsupported antenna ports: 5G networks will support much larger antennaarrays to enable new “massive” MIMO precoding/beamforming strategiesthat aim to provide, among other things, the following gains:

-   -   enhanced beamforming gains for SU-MIMO systems (e.g., improve        receive power at a desired UE, and simultaneously reduce        interference to other UEs);    -   enhanced spatial multiplexing for SU-MIMO systems (e.g., improve        throughput and/or reliability by spatially multiplexing        two-or-more data-streams to a desired UE on individual OFDM        resource blocks), and    -   enhanced MU-MIMO capabilities (e.g., improve spectral efficiency        by spatially multiplexing two-or-more UEs on individual OFDM        resource blocks).

Advanced MIMO precoding/beamforming techniques require accurateknowledge of the channel H at the BSs and/or an interference-plus-noisecovariance matrix Σ_(z).

Poor channel state information (CSI) at a BS significantly limits itsability to accurately beamform and spatially multiplex data.

Time division duplex (TDD) network deployments in which uplink-downlinkchannel reciprocity holds are an ideal candidate for 5G systems and MIMOprecoding/beamforming. Uplink-downlink channel reciprocity implies thatthe BS can directly estimate the downlink channels from uplink referencesignals (e.g., SRS).

If uplink-downlink channel reciprocity holds, then it is sometimespossible for the BS to obtain accurate CSI with relatively small (uplinkspectral efficiency) cost to the network.

Uplink-downlink channel reciprocity will not hold for all deployments.For example, channel reciprocity does not hold for frequency divisionduplex (FDD) deployments, where uplink and downlink transmissions occuron different carriers. FDD deployments are often required for coverage,legacy and regulatory reasons.

Uplink-downlink reciprocity does not hold for all TDD systems either.For example, UEs have much smaller transmit power capabilities (due toregulatory and battery limitations), and, therefore, the coverage ofuplink reference signals (e.g., SRS) is much smaller than the coverageof downlink reference signals (e.g., CSI-RS). Moreover, the Rel. 15standard mandates the use of 4 antenna ports for downlink reception, butonly one antenna for uplink transmission. Thus, at least in the currentstandard, there may be a sounding mismatch between the uplink anddownlink.

Full uplink-downlink channel reciprocity will for example not hold forall 5G deployments.

If uplink-downlink channel reciprocity does not hold, then the BS canobtain CSI via 3GPP's standardized channel state information (CSI)feedback reporting mechanisms. The basic idea underlying these reportingmechanisms can be described as follows:

-   -   The BS transmits downlink reference signals (e.g., CSI-RS).    -   The UEs estimate their channels (or important parameters        thereof) using the downlink reference signals.    -   The UEs send CSI reports over the uplink to the BS.

CSI Feedback: Raw Channel Measurements

It is not possible for the UEs to include “raw” downlink channelestimates in its CSI reports—the resulting overhead would simplysuffocate the uplink.

As an example, suppose that we have a network configured to operate witha 10 MHz LTE carrier and 9 subbands, which is a common LTE deployment.Further suppose that we have a BS with N_(TX)=64 antenna ports and a UEwith 4 antenna ports. The number of complex channel matrix coefficientsis then N_(TX)N_(RX)N_(SB)=64×4×9=2304. If the UE quantizes eachcoefficient using 10 bits and the network uses a 10 msec CSI reportingperiod, then resulting uplink CSI overhead would be 2304*10*100=2.304Mbps (or, equivalently, 0.2304 bps/Hz spectral efficiency). As abenchmark, the ITM2020 minimum uplink cell-edge spectral efficiencyrequirements are defined as follows (Ericsson, 2018):

-   -   5G dense urban macro-layer deployments (200 m ISD, 39 AP, 4        GHz)=0.225 bps/Hz    -   5G rural (1732 m ISD, 39AP, 4 GHz)=0.12 bps/Hz    -   5G indoor hotspot (20 m ISD, 12 Aps, 30 GHz)=0.3 bps/Hz.

It is not possible to include raw channel matrix estimates (or channelcovariance matrix estimates) and achieve reasonable quantizationaccuracy in the uplink CSI reports.

CSI Feedback: 3GPP Implicit Type I and Type II

To reduce uplink overhead, 3GPP LTE and 5G networks only require UEs tofeedback CSI that is relevant to the upcoming scheduling decisions atthe serving BS. The basic principle of the NR CSI feedback is asfollows. The BS first configures the UE's CSI report. This configurationmay specify the time- and frequency-resources that can be used by the UEto report CSI as well as what information should be reported. Forexample, the CSI report can consist of a channel quality indicator(CQI), precoding matrix indicator (PMI), CSI-RS resource indicator(CRI), strongest layer indication (SLI), rank indicator (RI), and/orL1-RSRP.

3GPP Release 15 specifies two types of CSI reports, each utilizingdifferent ways to calculate the PMI (i.e. different “codebooks”)

-   -   Type I CSI feedback, and    -   Type II CSI feedback.

Type I CSI feedback has been designed as a low-feedback overheadtechnology, primarily for SU-MIMO scenarios. The Type I precodercodebooks are based on DFT vectors, where a spatial layer of theprecoder only utilizes a single DFT vector, corresponding to only thestrongest angular direction of the channel. Thus, such a codebook may beseen as a spatial downsampling of the channel.

Type-I feedback is mostly useful for SU-MIMO operation. To enable moreadvanced features (e.g., non-linear precoding or MU-MIMO), more advancedMIMO precoding techniques are required. These techniques require moredetailed channel knowledge at the BS.

Type II CSI feedback has been designed to provide higher resolutionchannel knowledge at the BS, albeit with higher feedback overhead. It isenvisaged that Type-II CSI will enable the BS to perform more advancedMU-MIMO precoding techniques (such as zero-forcing and regularizedzero-forcing) needed for MU-MIMO.

According to 3GPP TR 38.802 V14.2.0, type II feedback consists ofexplicit feedback and/or codebook-based feedback with higher spatialresolution. At least one scheme from Category 1, 2 and/or 3 for Type IICSI is supported. Category 1 is described below as an example.Categories 2 and 3 are not described herein.

Category 1: Precoder feedback based on linear combination codebookdual-stage W=W₁W₂. Here codebook W₁ consists of a set of L orthogonalbeams taken from the 2D DFT matrix. The set of L beams is selected outof a basis composed of oversampled 2D DFT beams, where L∈{2, 3, 4} (L isconfigurable) and beam selection is wideband. As for W₂: The L beams arecombined in W₂ with common W₁ subband reporting of phase quantization ofbeam combining coefficients (configurable between QPSK and 8-PSK phaserelated information quantization). Beam amplitude scaling quantizationcan be configured for wideband or subband reporting.

Category 1 CSI feedback will only provide high resolution channel stateinformation if most of the channel's energy is contained within L DFTbasis vectors. Or, put another way, the channel needs to be L-sparsewith respect to the DFT unitary rotation. Not all channels for alldeployments are sparse with respect to the DFT matrix.

CSI Feedback: Channel Averaging

Instead of feeding-back raw channel estimates over the uplink, the UEcan send averages of these channel estimates over several subbands (orresource blocks). For example, in an example scenario with 9 subbands,it is possible to reduce the uplink overhead by a factor of 9 byaveraging the channels over all 9 subbands.

Let H₁, H₂, . . . , H_(N) _(SB) denote the channels of a single UE overN_(SB) subbands. Let

$R_{TX}:=\frac{1}{N}{\sum\limits_{n = 1}^{N_{RB}}{H_{n}^{*}H_{n}}}$ and$R_{RX}:=\frac{1}{N}{\sum\limits_{n = 1}^{N_{SB}}{H_{n}H_{n}^{*}}}$respectively denote the sample average transmitter-side andreceiver-side covariance matrices. Let R_(TX)=V ΣV* and R_(RX)=U ΣU*denote the corresponding singular value decomposition of the abovesample covariance matrices. Let U[1: N_(RX), :] denote the first N_(RX)singular vectors in U, and suppose that U[1: N_(RX), :] and V arefeedback to the BS over the uplink. The BS can then approximate theaverage channel byĤ:=U[1:N _(RX),:]√ΣV*

Channel averaging over the entire bandwidth is required to reduce uplinkoverhead to a feasible level. However, such extreme averaging leads topoor downlink performance (the spatial information contained within thechannel covariance matrix of a given subband is averaged-out). Further,the UE is required to perform singular-value matrix decompositions ofthe transmitter- and receiver-side sample covariance matrices R_(TX) andR_(RX) for every CSI report (e.g. every 10 msecs). The complexity ofthis decomposition for an (m×n) matrix is approximately O(mn²), which isa non-trivial computational and energy cost on the UE.

CSI Feedback: Transform-Domain Dimensionality Reduction

It is often possible to transform the channel matrices into a “sparsebasis” where most of the channel's energy is concentrated in a fewimportant dimensions. The Discrete Fourier Transform (DFT) is a commonlyused transform: The DFT matrix is a unitary transform, and line-of-sightchannels are approximately sparse under this transform (rich scatteringnon-LoS channels are not sparse under the DFT transformation). Othertransforms include the Discrete Cosine Transform (DCT) and variouseigen-decompositions.

CSI Feedback: Compressive-Sensing Dimensionality Reduction

If the channels are sparse in some basis, but the exact sparse basis isnot known, then it is possible to use compressive-sensing techniques(Candes, Romberg, & Tao, 2006) to automatically find and exploit thechannel's sparseness (Kuo, Kung, & Ting, 2012). The basic idea ofcompressed sensing is to take random linear projections of the channel:If the number of random projections exceeds the “sparsity” of thechannel, then it is possible to reconstruct the channel to within anarbitrarily small error (the reconstruction fidelity is guaranteed bythe restricted isometry property).

Decompressing the random linear projections in compressed sensingrequires the BS to solve computationally expensive convex optimizationproblems; for example, the complexity of recovering a vector of size nusing the basis pursuit algorithm is O(n³). If CS is used for eachuplink CSI report, then the BS will need to solve many suchoptimizations approximately every 10 msec. This is a non-trivialcomputational cost, when compared to similar operations (e.g., lineartransmitter-side beam forming and linear receiver filtering).

The computational requirements of such CS reconstruction algorithmswould increase the hardware requirements and cost of each BS and wouldadd significant decoding latency.

Conclusion

As described above, several ways have been proposed for how a terminaldevice (such as a UE) may convey information about downlink channelestimates to a network node. However, in order to address one or more ofthe above-mentioned issues, it would be desirable to provide a new wayto convey information about downlink channel estimates.

SUMMARY

Embodiments of methods, terminal devices, network nodes, computerprograms, computer program products, and non-transitorycomputer-readable media are provided herein for addressing one or moreof the abovementioned issues.

Hence, a first aspect provides embodiments of a method of operating aterminal device. The method comprises receiving a first set ofparameters, forming a compression function based on the first set ofparameters, compressing downlink channel estimates using the compressionfunction, and transmitting the compressed downlink channel estimates.The compression function comprises a first function, a second function,and a quantizer. The first function is formed based on at least some ofthe parameters from the first set of parameters. The first function isconfigured to receive input data, and to reduce a dimension of the inputdata. The second function is a non-linear function.

It will be appreciated that the first set of parameters comprisesmultiple parameters.

It will be appreciated that the compression function is employed toconvert or transform the downlink channel estimates into a compressedformat or representation.

Different compression functions may be suitable for compression of thedownlink channel estimates, for example depending on factors such asproperties of the terminal device itself, and/or properties of a device(such as a network node) which is intended to receive the compresseddownlink channel estimates. Since the compression function is formedbased on the first set of parameters, these parameters may be employedto control which compression function to be used at the terminal device.

A second aspect provides embodiments of a method of operating a networknode. The method comprises determining a first set of parameters. Thefirst set of parameters indicates a compression function for compressingdownlink channel estimates at a terminal device. The method comprisesdetermining a decompression function for decompressing downlink channelestimates which have been compressed by the terminal device using thecompression function. The method comprises transmitting the first set ofparameters, receiving compressed downlink channel estimates, anddecompressing the compressed downlink channel estimates using thedecompression function. The decompression function comprises a firstfunction, and a second function. Determining the decompression functioncomprises determining the first function. The first function isconfigured to receive input data and to provide output data in a higherdimensional space than the input data. The second function is anon-linear function.

It will be appreciated that the decompression function is employed toreconstruct or recreate the downlink channel estimates from thecompressed format or representation. It will also be appreciated thatthis recreation or reconstruction may not be perfect. In other words,the decompressed downlink channel estimates (in other words, thedownlink channel estimates as reconstructed or recreated at the networknode) may deviate from the original downlink channel estimates whichwere compressed before being received by the network node.

A third aspect provides embodiments of a terminal device. The terminaldevice is configured to receive a first set of parameters, form acompression function based on the first set of parameters, compressdownlink channel estimates using the compression function, and transmitthe compressed downlink channel estimates. The compression functioncomprises a first function, a second function, and a quantizer. Theterminal device is configured to form the first function based on atleast some of the parameters from the first set of parameters. The firstfunction is configured to receive input data, and to reduce a dimensionof the input data. The second function is a non-linear function.

The terminal device may for example be configured to perform the methodas defined in any of the embodiments of the first aspect disclosedherein (in other words, in the claims, or the summary, or the detaileddescription, or the drawings).

The terminal device may for example comprise processing circuitry and atleast one memory. The at least one memory may for example containinstructions executable by the processing circuitry whereby the terminaldevice is operable to perform the method as defined in any of theembodiments of the first aspect disclosed herein.

A fourth aspect provides embodiments of a network node. The network nodeis configured to determine a first set of parameters. The first set ofparameters indicates a compression function for compressing downlinkchannel estimates at a terminal device. The network node is configuredto determine a decompression function for decompressing downlink channelestimates which have been compressed by the terminal device using thecompression function. The network node is configured to transmit thefirst set of parameters, receive compressed downlink channel estimates,and decompress the compressed downlink channel estimates using thedecompression function. The decompression function comprises a firstfunction, and a second function. The network node is configured todetermine the decompression function by at least determining the firstfunction. The first function is configured to receive input data and toprovide output data in a higher dimensional space than the input data.The second function is a non-linear function.

The network node may for example be configured to perform the method asdefined in any of the embodiments of the second aspect disclosed herein(in other words, in the claims, or the summary, or the detaileddescription, or the drawings).

The network node may for example comprise processing circuitry and atleast one memory. The at least one memory may for example containinstructions executable by the processing circuitry whereby the networknode is operable to perform the method as defined in any of theembodiments of the second aspect disclosed herein.

A fifth aspect provides embodiments of system comprising a terminaldevice as defined in any of the embodiments of the third aspectdisclosed herein and a network node as defined in any of the embodimentsof the fourth aspect disclosed herein.

A sixth aspect provides embodiments of a computer program comprisinginstructions which, when executed by a computer, cause the computer toperform the method of any of the embodiments of the first aspectdisclosed herein.

A seventh aspect provides embodiments of a computer program productcomprising a non-transitory computer-readable medium, storinginstructions which, when executed by a computer, cause the computer toperform the method of any of the embodiments of the first aspectdisclosed herein.

An eighth aspect provides embodiments of a non-transitorycomputer-readable medium storing instructions which, when executed by acomputer, cause the computer to perform the method of any of theembodiments of the first aspect disclosed herein.

A ninth aspect provides embodiments of a computer program comprisinginstructions which, when executed by a computer, cause the computer toperform the method of any of the embodiments of the second aspectdisclosed herein.

A tenth aspect provides embodiments of a computer program productcomprising a non-transitory computer-readable medium, storinginstructions which, when executed by a computer, cause the computer toperform the method of any of the embodiments of the second aspectdisclosed herein.

An eleventh aspect provides embodiments of a non-transitorycomputer-readable medium storing instructions which, when executed by acomputer, cause the computer to perform the method of any of theembodiments of the second aspect disclosed herein.

The effects and/or advantages presented in the present disclosure forembodiments of the method according to the first aspect may also applyto corresponding embodiments of the method according to the secondaspect, the terminal device according to the third aspect, the networknode according to the fourth aspect, the system according to the fifthaspect, the computer program according to the sixth or ninth aspect, thecomputer program product according to the seventh or tenth aspect, andthe non-transitory computer-readable medium according to the eighth oreleventh aspect. Similarly, the effects and/or advantages presented inthe present disclosure for embodiments of the method according to thesecond aspect may also apply to corresponding embodiments of the methodaccording to the first aspect, the terminal device according to thethird aspect, the network node according to the fourth aspect, thesystem according to the fifth aspect, the computer program according tothe sixth or ninth aspect, the computer program product according to theseventh or tenth aspect, and the non-transitory computer-readable mediumaccording to the eighth or eleventh aspect.

It is noted that embodiments of the present disclosure relate to allpossible combinations of features recited in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In what follows, example embodiments will be described in greater detailwith reference to the accompanying drawings, on which:

FIG. 1 shows an example uniform linear array of antenna elements;

FIG. 2 shows an example uniform planar array of antenna elements;

FIG. 3 is a flow chart a method of operating a terminal device,according to an embodiment;

FIG. 4 is a flow chart a method of operating a network node, accordingto an embodiment;

FIG. 5 shows signals transmitted between a network node and a terminaldevice, according to an embodiment;

FIG. 6 shows a schematic representation of a compression function,according to an embodiment;

FIG. 7 shows a schematic representation of a decompression function,according to an embodiment;

FIG. 8 illustrates an example scheme for determining a decompressionfunction in the method in FIG. 4 , according to an embodiment;

FIG. 9 is a flow chart of a method of operating a network node,including transmission of parameters indicating a decompressionfunction, according to an embodiment;

FIG. 10 is a flow chart of a method of operating a terminal device,including determination of an updated compression function, according toan embodiment;

FIG. 11 shows signals transmitted between a network node and a terminaldevice, including transmission of parameters indicating a decompressionfunction, according to an embodiment;

FIG. 12 is a flow chart a method of operating a network node, includingselection of parameters based on evaluation of compression functions anddecompression functions, according to an embodiment;

FIG. 13 is a flow chart a method of operating a network node, includingupdating of parameters based on evaluation of a compression function anda decompression function, according to an embodiment;

FIG. 14 shows signals transmitted between a network node and a terminaldevice, including transmission of an updated parameter from the terminaldevice, according to an embodiment;

FIG. 15 shows a compression function and a decompression functionprovided in the form of a neural network, according an embodiment;

FIG. 16 shows a wireless network in accordance with some embodiments;and

FIG. 17 shows performance of different compression methods.

All the figures are schematic, not necessarily to scale, and generallyonly show parts which are necessary in order to elucidate the respectiveembodiments, whereas other parts may be omitted or merely suggested. Anyreference number appearing in multiple drawings refers to the sameobject or feature throughout the drawings, unless otherwise indicated.

DETAILED DESCRIPTION 1. Problems with Existing Solutions

3GPP networks have standardized Type-I and Type-II CSI feedback.

Type-I CSI feedback has a low uplink overhead cost, but it cannot beused for advanced downlink precoding (e.g., MU-MIMO precoding and SUnon-linear precoding).

Type-II CSI feedback provides more detailed CSI knowledge to the BS withincreased uplink overhead. Type-II CSI requires the UE to report the Lstrongest projections of the channel matrix onto a standardized 2D DFTprecoding/beamforming matrix. The 2D DFT matrix exploits the structureof the BS's ULA or UPA. If

-   -   the antenna ports on the ULA or UPA are not perfectly spaced        (e.g., 0.5 wavelength for a ULA), and/or    -   the array is operated at several different frequencies, and/or    -   the array is not perfect calibrated, and/or,    -   the array has severe mutual coupling or other types of        impairments,        then the ULA/UPA model is not perfectly accurate anymore and the        sparsity of the DFT basis may be destroyed.

A channel state (CS) compression method that is robust tochanges/imperfections in the BS's array would be preferable.

Other explicit CSI feedback techniques are channel averaging, sparsetransforms, and compressive sensing. These methods are not currentlysuitable for practical networks. Some shortcomings are discussed in thebackground section.

Moreover, the transform-domain approach described in the backgroundsection implicitly assumes that an appropriate “sparse basis” for theUEs exists and is known for a given deployment. Unfortunately, inpractice, it is unlikely that the channels across different deployments,operating in vastly different environments with different equipment,will exhibit the same (or similar) sparsity properties. Indeed, 5Gsupports many new use cases and UE types (e.g., drones, cars, IoTdevices) with different antenna configurations and channel conditions.

2. General Embodiments of Methods of Operating a Terminal Device andMethods of Operating or a Network Node

FIG. 3 is a flow chart a method 300 of operating a terminal device,according to an embodiment. The terminal device may for example bereferred to as a communication device. The terminal device may forexample be a user equipment (UE). UE refers to any type of wirelessdevice communicating with a network node and/or with another UE in acellular or mobile communication system. Examples of UE are targetdevice, device to device (D2D) UE, V2X UE, ProSe UE, machine type UE orUE capable of machine to machine (M2M) communication, PDA, iPAD, Tablet,mobile terminals, smart phone, laptop embedded equipped (LEE), laptopmounted equipment (LME), USB dongles etc. Example implementations of aterminal device will be described further below with reference to FIG.16 .

The method 300 comprises receiving 302 a first set of parameters. Thefirst set of parameters comprises multiple parameters, for example inthe form of real or complex numbers. In some of the examples presentedbelow, the first set of parameters is denoted by a. The first set ofparameters may for example be received 302 via radio resource control(RRC). The first set of parameters may for example beconfigured/selected by a network node and communicated to the terminaldevice using a CSI report configuration via the RRC protocol (3GPP TS38.331 V15.4.0).

The method 300 comprises forming (or generating) 303 a compressionfunction (which may also be referred to as a compressor) based on thefirst set of parameters, and compressing 304 downlink channel estimatesusing the compression function. In other words, the compression functionis employed to compress the downlink channel estimates into a morecompact or bitrate-efficient format or representation. The downlinkchannel estimates may for example be compressed jointly or together,rather than being compressed individually.

The method 300 comprises transmitting 305 the compressed downlinkchannel estimates. The compressed downlink channel estimates may forexample be transmitted 305 via Physical Uplink Shared Channel (PUSCH) orPhysical Uplink Control Channel (PUCCH) to a network node.

The compression function formed or generated at step 303 comprises afirst function, a second function, and a quantizer (which may also bereferred to as a quantization function). In other words, using thecompression function at step 304 comprises using the first function, thesecond function and the quantizer. The first function, the secondfunction, and the quantizer may for example be regarded as parts orsubfunctions of the compression function. The second function is anon-linear function. The quantizer is configured to performquantization.

The first function is formed based on at least some of the parametersfrom the first set of parameters. In other words, the first function isnot predefined, and can be at least partially controlled via the firstset of parameters. The first function is configured to receive inputdata, and to reduce a dimension of the input data. In other words, dataof a first dimension may be inserted into the first function and may bereduced by the first function into data of a second dimension which islower than the first dimension.

The first function serves to reduce a dimensionality of the downlinkchannel estimates. Different compression functions may be suitable forcompression of the downlink channel estimates, for example depending onfactors such as properties of the terminal device itself, and/orproperties of a device (such as a network node) which is intended toreceive the compressed downlink channel estimates. Since the compressionfunction is formed based on the first set of parameters, theseparameters may be employed to control which compression function to beused at the terminal device. As described below (see for example theautoencoder example described below with reference to FIG. 15 ), thesecond function may for example enhance a training process to findsuitable values for the first set of parameters (and thereby also asuitable compression function). For example, such training may beenhanced compared to a situation where only linear functions areemployed in the compression function. For example, suitable values forthe first set of parameters can be found (or optimized) by a networknode during a training process. The resulting compression performance(such as the fidelity of downlink channel estimates reconstructed at thenetwork node after having been compressed at the terminal device usingthe compression function) of the optimized first parameters will dependon the choice of the second function. The second function introducesnonlinearities into the compression function that allow the downlinkchannel estimates to be better approximated (see for example theperformance described below with reference to FIG. 17 ). The quantizerserves to provide output data that can be transmitted using a finitenumber of bits.

The downlink channel estimates to be compressed at the step 304 may forexample be determined 301 as part of the method 300. For example, themethod 300 may comprise determining 301 the downlink channel estimatesusing downlink reference signals.

FIG. 4 is a flow chart a method 400 of operating a network node,according to an embodiment. The network node may for example be referredto as a base station and may correspond to any type of radio networknode or any network node, which communicates with a terminal device (orUE) and/or with another network node. Examples of network nodes areNodeB, base station (BS), multi-standard radio (MSR) radio node such asMSR BS, eNodeB, gNodeB. MeNB, SeNB, network controller, radio networkcontroller (RNC), base station controller (BSC), road side unit (RSU),relay, donor node controlling relay, base transceiver station (BTS),access point (AP), transmission points, transmission nodes, RRU, RRH,nodes in distributed antenna system (DAS), core network node (e.g. MSC,MME etc), O&M, OSS, SON, positioning node (e.g. E-SMLC) etc. Exampleimplementations of the network node will be described further below withreference to FIG. 16 .

The method 400 comprises determining 401 a first set of parameters. Thefirst set of parameters indicates a compression function for compressingdownlink channel estimates at a terminal device. The first set ofparameters is the same set of parameters as the first set of parametersreceived at step 302 of the method 300.

The method 400 comprises determining 402 a decompression function (whichmay also be referred to as a decompressor) for decompressing downlinkchannel estimates which have been compressed by the terminal deviceusing the compression function. In other words, the decompressionfunction is determined so as to be suitable for use together with thecompression function.

The method 400 comprises transmitting 403 the first set of parameters.The first set of parameters may for example be transmitted 403 via radioresource control (RRC) to a terminal device.

The method 400 comprises receiving 404 compressed downlink channelestimates, and decompressing 405 the compressed downlink channelestimates using the decompression function. In other words, thedecompression function is employed to reconstruct or recreate downlinkchannel estimates which have been compressed at a terminal device. Itwill be appreciated that the reconstruction of the downlink channelestimates may not be perfect, and that the downlink channel estimates asreconstructed may deviate from the original downlink channel estimates.The compressed downlink channel estimates are the same as thosetransmitted at step 304 in the method 300.

The decompression function determined at step 402 comprises a firstfunction and a second function. In other words, using the decompressionfunction comprises using the first function and the second function ofthe decompression function. The first function and the second functionof the decompression function may for example be regarded as parts orsubfunctions of the decompression function. The first function and thesecond function of the decompression function typically do not coincidewith the first function and the second function of the compressionfunction formed at step 302 of the method 300.

The step 402 of determining the decompression function comprisesdetermining the first function of the decompression function. In otherwords, the first function of the decompression function is notpredefined.

The first function of the decompression function is configured toreceive input data and to provide output data in a higher dimensionalspace than the input data. In other words, the first function isconfigured to receive data of a first dimension, for example in the formof N real numbers, and to output data in a space of a second dimensionwhich is higher than the first dimension, for example in the form of Mreal numbers where M is larger than N.

The second function of the decompression function is a non-linearfunction.

As described above, different compression functions may be suitable forcompression of the downlink channel estimates, for example depending onfactors such as properties of the terminal device itself, and/orproperties of a device (such as a network node) which is intended toreceive the compressed downlink channel estimates. In the method 400,the network node may determine which compression function to be employedat a terminal device, and may signal this to the terminal device via thefirst set of parameters.

In analogy with the second function of the compression function formedat step 303 of the method 300, the second function of the decompressionfunction may for example enhance a training process to find a suitabledecompression function. For example, such training may be enhancedcompared to a situation where only linear functions are employed in thedecompression function. For example, a suitable compression function anda suitable decompression function can be found (or optimized) by anetwork node during a training process. The resulting compressionperformance (such as the fidelity of downlink channel estimatesreconstructed at the network node after having been compressed at theterminal device using the compression function) of the compressionfunction and decompression function will depend on the choice of thesecond function of the decompression function. The second function ofthe decompression function introduces nonlinearities into thedecompression function that allow the downlink channel estimates to bebetter approximated.

FIG. 5 shows signals transmitted between a network node 501 and aterminal device 502, when the network node 501 performs the method 400described above with reference to FIG. 4 and the terminal device 502performs the method 300 described above with reference to FIG. 3 .Hence, the network node 501 transmits 403 the first set of parameters503, and the terminal device 502 receives it. The terminal device 502transmits 305 the compressed downlink channel estimates 504, and thenetwork node 501 received them.

According to some embodiments, the downlink channel estimates (which arecompressed at step 304 of the method 300) comprise information aboutchannels from antenna ports of the network node 501 to antenna ports ofthe terminal device 502. The downlink channel estimates may for exampleinclude the matrix H and/or the vector z described above in thebackground section. The downlink channel estimates may for exampleinclude a rank indicator (RI) and/or a channel quality indicator (CQI).

According to some embodiments, the second function of the compressionfunction and/or the second function of the decompression function maycomprise a non-linear activation function. Activation functions areoften employed in machine learning, such as neural networks. Anactivation function may provide a “threshold” that allows things to be“tuned on” (i.e. activated) or “turned off” (i.e. deactivated). Theactivation function may be a scalar non-linear function (i.e., y=f(x)where x and y are real numbers). A simple example might be f(x)=0 ifx<=0 and f(x)=1 if x>0. This function turns x off whenever it isnegative. Another example is the sigmoid function function. Theactivation function comprised in the compression function may forexample be the same activation function as the activation functioncomprised in the decompression function, or these two activationfunctions may be different activation functions.

According to some embodiments, the second function of the compressionfunction and/or the second function of the decompression function arepredefined. In other words, the second function of the compressionfunction and/or the second function of the decompression may be known inadvance by the terminal device and/or the network node.

According to some embodiments, the first function of the compressionfunction is configured to output a plurality of numbers, and the secondfunction of the compression function is configured to apply a scalarnon-linear function to each of the plurality of numbers. The scalarnon-linear function may for example be an activation function. Anexplicit example implementation of such first and second functions arethe functions 1508 and 1511 in the autoencoder example described belowwith reference to FIG. 15 .

According to some embodiments, the first function of the decompressionfunction is configured to output a plurality of numbers, and the secondfunction of the decompression function is configured to apply a scalarnon-linear function to each of the plurality of numbers. The scalarnon-linear function may for example be an activation function. Anexplicit example implementation of such first and second functions arethe functions 1516 and 1518 in the autoencoder example described belowwith reference to FIG. 15 .

According to some embodiments, the first function of the compressionfunction is a linear function, or the first function of the compressionfunction comprises is a linear portion (or linear part) and a constantportion (or a constant part). In other words, in addition to having alinear portion, first function of the compression function may comprisea constant portion which is not affected by input data received by thefirst function. The constant portion may for example be provided in theform of a constant term or bias.

According to some embodiments, the first function of the decompressionfunction is a linear function, or the first function of thedecompression function comprises is a linear portion (or linear part)and a constant portion (or a constant part). In other words, in additionto having a linear portion, first function of the decompression functionmay comprise a constant portion which is not affected by input datareceived by the first function. The constant portion may for example beprovided in the form of a constant term or bias.

According to some embodiments, the quantizer of the compression functionis configured to receive a plurality of numbers (for example from thesecond function of the compression function), and to apply scalarquantizers to the received numbers. The scalar quantizers may forexample be stochastic quantizers, as described below in section 6.2.7.

FIG. 17 shows performance of four different compression methods on alarge set of real downlink-channel estimates, which were obtained by areceiver on a car driving around in an area with buildings. In thepresent example, each channel estimate consists of 120 different values(120 complex numbers). Four different methods were used to compress eachdownlink-channel estimate from its original 120 values down to 5 values(e.g., for uplink transmission), and then to reconstruct it back to 120values. The performance of each compression method is characterized by acumulative distribution function (CDF) of the mean squared error (MSE)of the reconstruction. The CDF curves describe the % of channels thatachieve a certain MSE. For example, the solid curve 1703 achieved a MSEof 0.14 or less for 50% of the measured channels. The channels are allnormalized to one, so 100% channels achieve an MSE of 1 or less.

The * curve 1701 is the standard compression method using only linearfunctions (two matrix multiplications W1*W2). Its performance is quitebad.

The + curve 1702 adds a nonlinear function between the matrices W1 andW2 (the nonlinear function is a linear rectifier unit). The compressionperformance is much better because the nonlinear function allows “bad”linear projections in W1 to be removed and/or repurposed to “good”linear projections that better match the channel estimate.

The solid curve 1703 is the “idea genie aided” scheme in which theterminal device only tells the network node of the five largest valuesof the channel estimate. Here we assume a “genie” provides the locationof these largest values in the channel estimate to be network node forfree (so it is not really practical, but it's a good reference point).

The circle curve 1704 is for a more complicated nonlinear functioninvolving a neural network with nonlinear activation functions and manylayers of nodes. Its performance is better than for the othercompression methods in FIG. 17 .

In the example described above with reference to FIG. 17 , theparameters of the compression methods (such as the first set ofparameters a received at step 302 in the method 300) weretrained/designed/optimized only using simulated synthetic downlinkchannel estimates in a simulator (and not using the real measurementsfrom the car). So the curves in FIG. 17 suggest that nonlinearcompression functions and decompression functions can be designed“offline” using synthetic simulator data.

According to some embodiments, the first set of parameters and thedecompression function are determined in the method 400 based on:

-   -   information about the terminal device 502; and/or    -   information about the network node 501 and/or    -   information about a cell of the network node 501.

In other words, the step 401 of determining the first set of parameters,and the step 402 of determining the compression function may be based onany of the above factors. This allows the compression of the downlinkchannel estimates to be tailored to the specific circumstances, ratherthan being the same for all network nodes 501, terminal devices 502 andcells in a communication network.

The network node may for example have access to a list (or database) ofsuitable compression functions (for example defined in the list viarespective values for the first set of parameters) and decompressionfunctions (for example defined in the list via respective values for asecond set of parameters) to be used in different scenarios. The networknode may for example select among the options available in the listbased on the above listed factors.

According to some embodiments, the first set of parameters and thedecompressor are determined in the method 400 based on:

-   -   a position of the network node; and/or    -   a position of the terminal device; and/or    -   an expected pathloss for the terminal device; and/or    -   a precoding method used by the network node; and/or    -   a type of preceding used to transmit downlink reference signals;        and/or    -   a time and/or frequency granularity of channel state        information, CSI, related measurements and reporting.

In other words, the step 401 of determining the first set of parameters,and the step 402 of determining the compression function may be based onany of the above factors. For example, the compression function and thedecompression function may be designed differently for codebook-basedprecoding (e.g., using a Rel 15 downlink MIMO codebook) versus fornon-codebook-based precoding (e.g., zero-forcing precoding where thenetwork node tries to invert the channel matrix). Further examples offactors which may be taken into account when making these determinationsare provided below in section 6.2.3.

3. Alternating Sequence of Two Types of Functions

In the autoencoder example described below with reference to FIG. 15 ,the compression function 1501 only has a single part 1508 defined viathe first set of parameters a and a single non-linear part 1511 inaddition to the quantizer 1513. However, the compression function couldcomprise an alternating sequence of such parts, corresponding to anartificial neural network with more layers, where weighted sums areformed at the nodes, and where non-linear activation functions areapplied to the weighted sums. The weights employed in the weighted sumsmay be controller via the first set of parameters a.

FIG. 6 shows a schematic representation of such a compression function,according to an embodiment. In the present embodiment, the compressionfunction formed at step 303 of the method 300 comprises an alternatingsequence 600 of a first type of functions 601 and a second type offunctions 602. At least some of the first type of functions 601 areformed based on parameters from the first set of parameters. The secondtype of functions 602 are non-linear functions. In addition to thealternating sequence 600, the compression function also comprises aquantizer 603.

According to some embodiments, the order of the functions in thealternating sequence 600 of the first type of functions 601 and thesecond type of functions 602 is predefined. In other words, the order ofthe functions in the sequence 600 is not affected by the values of thefirst set of parameters a.

According to some embodiments, the first type of functions 601 are

-   -   linear functions; or    -   functions comprising a linear portion and a constant portion.

In other words, the first type of functions 601 could have a constantportion (or bias portion) in addition to a linear portion, in analogywith the function 1508 in FIG. 15 . If there is no constant portion (orbias), the first type of functions 601 are linear functions.

According to some embodiments, the second type of functions 602 arepredefined. In other words, the second type of functions 602 are notaffected by the values of the first set of parameters a.

In analogy with the compression function shown FIG. 6 , thedecompression function could comprise an alternating sequence offunctions of a first type and functions of a second type. FIG. 7 shows aschematic representation of such a decompression function, according toan embodiment. In the present embodiment, the decompression functiondetermined at step 402 comprises an alternating sequence 700 of a firsttype of functions 701 and a second type of functions 702. The secondtype of functions 702 are non-linear functions. The step 402 ofdetermining the decompression function comprises determining at leastsome of the first type of functions 701. In other words, at least someof the first type of functions 701 are not predefined. In contrast tothe compression function shown in FIG. 6 , the decompression functiondoes not comprise a quantizer.

According to some embodiments, the order of the functions in thealternating sequence 700 of the first type of functions 701 and thesecond type of functions 702 is predefined.

According to some embodiments, the first type of functions 701 are

-   -   linear functions; or    -   functions comprising a linear portion and a constant portion.

In other words, the first type of functions 701 could have a constantportion (or bias portion) in addition to a linear portion, in analogywith the function 1516 in FIG. 15 . If there is no constant portion (orbias), the first type of functions 701 are linear functions.

According to some embodiments, the second type of functions 702 arepredefined. In other words, the second type of functions 602 are knownin advance by the network node 501.

4. Use of a Second Set of Parameters

FIG. 8 illustrates an example scheme for determining the decompressionfunction in the method 400 described above with reference to FIG. 4 ,according to an embodiment. In the present embodiment, the step 402 ofdetermining the decompression function comprises determining 801 asecond set of parameters, and forming 802 the decompression functionbased on the second set of parameters. In other words, the second set ofparameters are employed (or used) to create or generate thedecompression function. It will be appreciated that the second set ofparameters comprises multiple parameters. In some examples describedbelow, the second set of parameters is denoted by b.

FIG. 9 is a flow chart of a method 900 of operating a network node,according to an embodiment. The method 900 comprises the steps 401-405from the method 400 described above with reference to FIG. 4 , where thestep 402 comprises the steps 801-802 described above with reference toFIG. 8 . The description of those steps will not be repeated here. Themethod 900 further comprises transmitting 901 the second set ofparameters. The second set of parameters may be received and employed bya terminal device, as described below with reference to FIG. 10 .

FIG. 10 is a flow chart of a method 1000 of operating a terminal device,according to an embodiment. The method 1000 comprises the steps 302-305from the method 300 described above with reference to FIG. 3 . Thedescription of those steps will not be repeated here. The method 1000further comprises receiving 1001 a second set of parameters. The secondset of parameters indicates a decompression function for decompressingdownlink channel estimates which have been compressed using thecompression function. The second set of parameters is the same set ofparameters as the second set of parameters transmitted at step 1201 inthe method 900.

The method 1000 comprises determining 1002, based on the first set ofparameters and the second set of parameters, an updated value for atleast one parameter from the first set of parameters, and forming 1004an updated compression function based on the updated value. The terminaldevice may for example detect or determine that the compression functionindicated via the first set of parameters is not optimal in combinationwith the decompression function indicated via the second set ofparameters. Performance of the compression function and thedecompression function may for example be evaluated as described belowin section 6.2.2. The terminal device may determine of compute updatedvalues for one of more of the first set of parameters, so as to obtain amore suitable compression function.

The method 1000 comprises compressing 1005 downlink channel estimatesusing the updated compression function, and transmitting 1006 thedownlink channel estimates compressed using the updated compressionfunction. In other words, the terminal device may employ the newcompression function instead of the compression function formed at step303.

FIG. 11 shows that the second set of parameters 1101 may be transmitted901 from the network node 501 to the terminal device 502, and that thenew compressed downlink channel estimates 1102 (in other words, thosewhich were compressed 1005 using the new compression function) may betransmitted 1006 from the terminal device 502 to the network node 501.

The methods 900 and 1000 in FIGS. 9 and 10 include several optionalsteps. These optional steps will be described also with reference toFIG. 11 , which shows signals which may be transmitted between thenetwork node 501 and the terminal device 502 if such optional steps areemployed.

According to an embodiment, the method 900 comprises transmitting 903 athird set of one or more parameters 1103. The third set of one or moreparameters 1103 indicates an objective function for evaluatingperformance of the compression function. In this scenario, the method1000 comprises receiving 1007 the third set of one or more parameters1103. In the present embodiment, the updated value for at least oneparameter from the first set of parameters is determined at step 1002using the objective function. In some examples described below, thethird set of one or more parameters is denoted by d.

The objective function may for example be a cost function or a lossfunction for evaluating whether downlink channel estimates aftercompression and decompression are similar to the original downlinkchannel estimates, as described below in section 6.2.2. If the deviationbetween the reconstructed values and the original values is too large,it may be a good idea to update the compression function and/or thedecompression function. While cost functions may be employed to definean optimization problem in which the cost function is to be minimized,it will be appreciated that such an optimization problem may easily bereformulated into an equivalent optimization where an objective functionequal to the cost function multiplied by −1 is to be maximized. In otherwords, the objective function need not necessarily be a cost functionwhich is to be minimized.

The method 900 may for example comprise the step 902 of determining thethird set of one or more parameters 1103. The third set of one or moreparameters 1103 (and thereby also the objective function) may forexample be determined based on information about the network node,and/or the terminal device. The third set of one or more parameters 1103(and thereby also the objective function) may for example be determinedbased on a precoder employed by the network node, as described below insection 6.2.2.

According to some embodiments, the method 1000 comprises determining1008, based on the first set of parameters and the second set ofparameters, an updated value for at least one parameter from the secondset of parameters, and transmitting 1009 the updated value for at leastone parameter from the second set of parameters. In the presentscenario, the method 900 comprises receiving 904 the updated value forat least one parameter from the second set of parameters and forming 905a second decompression function based on the updated value. In otherwords, the updated value is employed to generate a new decompressionfunction. Hence, the terminal device 502 indicates via the updated valuethat the network node 502 should use a different decompression function.The terminal device 502 may for example have detected that both thecompression function and the decompression function should be changed toimprove performance. FIG. 11 shows that the updated value 1104 for atleast one parameter from the second set of parameters may be transmitted1009 from the terminal device 502 to the network node 501.

The method 900 may for example comprise receiving 906 second compresseddownlink channel estimates 1102, and decompressing 907 the secondcompressed downlink channel estimates using the second decompressionfunction formed at step 905. The transmission of the second compresseddownlink channel estimates 1102 is illustrated in FIG. 11 .

5. Evaluation of Performance at the Network Node

FIG. 12 is a flow chart a method 1200 of operating a network nodeaccording to an embodiment. The method 1200 is similar to the method 400described above with reference to FIG. 4 , except that the steps ofdetermining 401 the first set of parameters and determining 402 thedecompression function are expressed in terms of three new steps1201-1203. More specifically, the method 1200 comprises evaluating 1201performance of different compression functions and decompressionfunctions using an objective function, selecting 1202 values for thefirst set of parameters and the second set of parameters based on theevaluation, and forming 1203 the decompression function based on thesecond set of parameters. In other words, values for the first set ofparameters and the second set of parameters are determined viaevaluation 1201 of different candidate compression functions andcandidate decompression functions. A suitable compression function and asuitable decompression function may for example be determined via theevaluation, and the values for the first and second sets of parametersmay for example be selected to indicate the suitable compressionfunction and the suitable decompression function, respectively. Theevaluation may for example be performed as described below in section6.2.2.

FIG. 13 is a flow chart a method 1300 of operating a network node,according to an embodiment. The method 1300 comprises the steps 401-405from the method 400 described above with reference to FIG. 4 . Thedescription of these steps will not be repeated here.

The method 1300 comprises evaluating 1301 performance of the compressionfunction (indicated by the first set of parameters determined at step401) and the decompression function (determined at step 402) using anobjective function. The objective function may for example be a costfunction of a loss function. The evaluation may for example be performedas described below in section 6.2.2.

The method 1300 comprises selecting 1302 a first updated value for atleast one parameter from the first set of parameters based on theevaluation, selecting 1303 a second updated value for at least oneparameter from the second set of parameters based on the evaluation,transmitting 1304 the first updated value, and forming 1305 an updateddecompression function based on the second updated value. In otherwords, the first updated value is transmitted 1304 for informing aterminal device that a new compression function is to be employed forcompression of downlink channel estimates. An updated decompressionfunction is formed 1305 based on the second updated value so thatdownlink channel estimates which have been compressed using the newcompression function can be decompressed in an appropriate way.

FIG. 14 shows signals which may be transmitted between the network node501 and the terminal device 502 when the method 1300 is performed. Thefirst updated value 1401 is transmitted 1304 from the network node 501to the terminal device 502. The first updated value 1501 is employed bythe terminal device 502 to form a new compression function. The terminaldevice 502 then employs the new compression function to compressdownlink channel estimates, and the compressed downlink channelestimates 1542 are transmitted from the terminal device 502 to thenetwork node 501.

According to some embodiments, the evaluation performed at step 1201 ofthe method 1200 or the evaluation performed at step 1301 may beperformed using a neural network.

6. Example Implementation 6.1 Overview

A new channel state compression (CSC) framework is proposed that enablesthe BS (provided herein as an example of a network node) to learn andexploit the unique channel statistics exhibited by UEs (provided hereinas examples of terminal devices) in its cell. The CSC framework can beincorporated within 3GPP's CSI reporting framework (Rel. 15) with fewchanges.

The proposed CSC framework includes the following three sets:

-   -   a configurable CS compressor comprising an alternating sequence        of linear- and non-linear operators (the CS compressor is a        finite-rate compressor in the sense that it compresses channel        state estimates into a format that can be conveyed using a        finite number of bits),    -   a configurable CS decompressor comprising an alternating        sequence of linear- and non-linear operators, and    -   a cost function.

An example of how the CSC framework can be used in a 3GPP network is asfollows:

-   -   1. When a new UE joins a cell, the serving BS associates the UE        with the following objects:        -   a. A configurable CS compressor (also referred to as a            compression function): The UE uses the CS compressor to            compress its raw CSI (for example, channel and/or            interference plus noise covariance matrix estimates) for            transmission to the BS over the uplink. In some embodiments,            the CS compressor is used by BS and/or UE for model-training            purposes.        -   b. A configurable CS decompressor (also referred to as a            decompression function): The BS uses the CS decompressor to            reconstruct the UE's compressed CSI. In some embodiments,            the CS decompressor is used by BS and/or UE for            model-training purposes.        -   c. A cost function (also referred to as an objective            function): The UE and BS can use the cost function in            different ways (for the different example embodiments            described below) to evaluate and train the compressor and            decompressor.    -   2. During the cell-association procedure, the BS may configure        and signal a specific CS configuration to the UE using one of        the following methods:        -   a. Explicitly: For example, the BS can signal to the UE a            specific compressor and decompressor configuration along            with a desired cost function using the radio resource            control (RRC), as part of the CSI reporting settings (3GPP            TS 38.214 V15.4.0). This capability enables the gNB to learn            and exploit unique statistical characteristics of its            environment and the UE type for compression. Different            example embodiments of the learning procedure are presented            below.        -   b. Implicitly: For example, the BS and UE may assume a            default cost function and/or a default configuration for the            CS compressor and CS decompressor until a new configuration            is determined. These default settings can be tailored to            match general propagation properties of the deployment, and            specific characteristics of the BS and UE that can be            determined from other means (for example, the UE category).    -   3. While connected to the BS, the BS and/or UE can evaluate the        compression performance and, if needed, train and update the        configuration of the CS compressor and decompressor. Different        update methods (e.g., periodic, aperiodic) as well as training        modes (e.g., gNB-side, UE-side, centralized, and de-centralized)        are outlined below.

6.2 Example Embodiment 1

6.2.1 Channel State Compression (CSC)

The 3GPP standard can be modified to define a class of channel statecompression (CSC) encoders,

, a matching class of CSC decoders

, and a class of cost functions

.

-   -   The CSC encoders can be used by the UEs to compress their raw        “explicit” channel matrices or tx-channel covariance matrices        for transmission over the uplink.    -   The CSC decoders can be used by the BSs to decompress the UEs'        channel estimates.    -   The cost functions can be used by the BS and/or UEs to measure        the performance of a given compressor-decompressor pair.

The encoder and decoder functions respectively take the formsƒ_(a):

^(N)→{1,2, . . . ,M}andg _(b):{1,2, . . . ,M}→

^(N)for some non-negative integers N and M.

The integer N implicitly defines how many complex channel coefficients(or, covariance matrix coefficients) are jointly compressed at the UE.For example, if we jointly compress the UE's channel estimates over the9 subbands over the frequency domain, thenN=N_(TX)N_(RX)N_(SB)=64×4×9=2304.

The integer M defines how many bits (i.e., [log₂(M)]) are fed back overthe uplink.

The subscripts a∈

and b∈

represent parameters that define the encoder ƒ_(a) and decoder g_(b)respectively (and, therefore, the integers N and M). a is a first set ofparameters, and b is a second set of parameters. d is a set of one ormore parameters.

The sets

,

and

represent finite sets from which the parameters a, b and d can

be selected respectively.

6.2.2 Performance Evaluation of CSC

Consider a compressor-decompressor-cost function tuple (ƒ_(a), g_(b),Δ_(d)) for some a∈

, b∈

and d∈

. Let (c₁, c₂, . . . , c_(N))∈

^(N) denote N downlink channel coefficient estimates to be compressedand fed back to the BS over the uplink. For example, these coefficientscan be the coefficients of one or more subcarrier (or, resource block)channel matrices spanning the whole band or a subband. Let(ĉ ₁ ,ĉ ₂ , . . . ,ĉ _(N)):=g _(b)(ƒ_(a)(c ₁ ,c ₂ , . . . ,c _(N)))denote the BS's reconstruction of these measurements using (ƒ_(a),g_(b)). The instantaneous performance of (ƒ_(a), g_(b)) on (c₁, c₂, . .. , c_(N)) can be quantified by the compression rate (measured inaverage number of bits per channel coefficient)

$R:=\frac{1}{N}\log_{2}M$and a reconstruction costΔ_(d):(ĉ ₁ ,ĉ ₂ , . . . ,ĉ _(N)),(c ₁ ,c ₂ , . . . ,c _(N)))

[0,∞].

Different embodiments of the proposed CSC framework employ differentcost functions. For example, basic candidate cost functions are theaverage square error distortions

${\Delta_{d}\left( {\left( {{\overset{\hat{}}{c}}_{1},{\overset{\hat{}}{c}}_{2},\ldots,{\overset{\hat{}}{c}}_{N}} \right),\left( {c_{1},c_{2},\ldots,c_{N}} \right)} \right)} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}{\left( {{\overset{\hat{}}{c}}_{n} - c_{n}} \right)^{2}.}}}$Or the average normalized square error distortions

${\Delta_{d}\left( {\left( {{\overset{\hat{}}{c}}_{1},{\overset{\hat{}}{c}}_{2},\ldots,{\overset{\hat{}}{c}}_{N}} \right),\ \left( {c_{1},c_{2},\ldots,c_{N}} \right)} \right)} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}\frac{\left( {{\overset{\hat{}}{c}}_{n} - c_{n}} \right)^{2}}{❘c_{n}❘}}}$

These cost functions are not ideal for MIMO precoding because theyenforce the reconstructions to be accurate in channel dimensions thatare not important for beamforming. More generally, it is important tocarefully choose the cost function to match the BS's intended usage ofthe CSI feedback. For example, if it is known that the BS will employ azero-forcing precoder to suppress inter-layer interference in MU-MIMO,then it would be appropriate to choose a suitable Δ_(d) to quantify thecost of choosing the wrong zero-forcing precoder (e.g., this could be a“throughput cost” with respect to zero-forcing precoding on the idealchannel).

In an example scenario, BS uses the uplink CSI to choose a downlink MIMOprecoder. A MIMO precoder is a matrix P that depends on thedownlink-channel estimate H. An important MIMO precoding method aims tochoose P to be a pseudo inverse of H, in other words to choose P suchthat HP=a diagonal matrix. The cost function employed in our proposedcompression approach could then be the MSE of reconstructing a “P” suchthat HP=diagonal, instead of the MSE of the original channel matrix H.The cost function could also add additional punishment to reconstructedP matrices that don't satisfy HP=diagonal (since off-diagonal elementscause inter-layer interference and, therefore, lower throughput to theUE).

In another example scenario, the MIMO precoder P is chosen from acodebook of precoders. In such cases the cost function employed in ourproposed compression approach could then be designed to punish “wrong”choices from the codebook, rather than the MSE of H.

The compression rate can be derived directly from the encoder ƒ_(a).Different embodiments can consider different knowledge of thecompressor, decompressor, and cost function at the BS and UE. Forexample, the following embodiments are possible:

-   -   ƒ_(a), g_(b) and Δ_(d) are known to both the BS and UE, or    -   ƒ_(a), g_(b) and Δ_(d) are known to the BS, and ƒ_(a) is known        to the UE.

6.2.3 Cell Association (Initialization of CSI Compressors)

When a new UE joins a cell, the serving BS may configure the UE CSIreporting settings as part of the RRC (see Section 5.2 in 3GPP TS 38.214V15.4.0). During this CSI configuration step, the BS can signal to theUE CSC parameters (a*, b*) and a cost function Δ_(d). Differentembodiments for this cell-association step are presented below.

The CSC parameters (a*, b*) can be tailored by the BS to, for example,match important attributes of the UE and the cell. For example, theparameters (a*, b*) can be tailored by the BS to match important factorsin the propagation environment. Such factors can include, for example,the following examples.

-   -   Cell-specific: For example, (a*, b*) can be chosen specifically        for the cell's propagation environment, considering, for        example, the carrier and numerology; the BS's antenna        configuration (panel and antenna layout); the spatial        distribution of UEs; and the multipath characteristics of the        channel.    -   UE-specific: For example, (a*, b*) can be chosen specifically to        exploit properties of the UE's antenna array (e.g., antenna        coherence, spacing and layout); and the computational abilities        of the UE. For example, a specific parameter set can be        specified for a given UE model.    -   Pathloss & precoding specific: (a*, b*) can be chosen        specifically to the UE's pathloss and expected rank. For        example, a UE located at the cell-edge will experience a large        pathloss and will likely only be scheduled with low rank in a        SU-MIMO manner. The parameters (a*, b*) can be tailored to match        the rank and expected codebook-based precoding mode.    -   CSI-RS-specific: For example, (a*, b*) can be designed        specifically to exploit the type of precoding used to transmit        the downlink reference signals (for example, beamformed CSI-RS        from non-coherent antenna ports or panels versus beamformed        CSI-RS from coherent antenna ports).    -   Time and/or frequency granularity of CSI-related measurements        and reporting: For example, (a*, b*) can be designed and chosen        to match the reporting frequency granularity of the CSI reports.

The parameters (a*, b*) can be preconfigured by the vendor or networkoperator based on, for example, laboratory experiments and/or fieldtrials with the BS equipment and UE equipment.

The parameters (a*, b*) can also be learned (or, periodically updated)by the BS using a supervised-learning or reinforcement-learning processthat exploits, for example, historical (or live or synthetic) channelmeasurements made elsewhere or within the BS's own cell. Suchmeasurements can contain raw complex channel coefficient measurements,pathloss measurements, channel-correlation measurements, information onthe spatial distribution of users, multipath statistics, etc. Differentembodiments of such learning processes are given below. Two key points:

-   -   The BS can use any information at its disposal to choose/design        (a*, b*),    -   The UE can use the cost function to collect statistics on the        compression performance to report back to the BS.

6.2.4 CSI Report Configuration

As described in 3GPP TS 38.214 V15.4.0 the UE is configured by higherlayers with N≥1 ReportConfig reporting settings, M≥1 ResourceConfigresource settings, and a single MeasConfig measurement settingcontaining L≥1 Links.

“Each Reporting Setting ReportConfig is associated with a singledownlink BWP (higher layer parameter bandwidthPartId) and contains thereported parameter(s) for one CSI reporting band: CSI Type (I or II) ifreported, codebook configuration including codebook subset restriction,time-domain behavior, frequency granularity for CQI and PMI, measurementrestriction configurations, the strongest layer indicator (SLI), thereported L1-RSRP parameter(s), CRI, and SSBRI (SSB Resource Indicator).Each ReportConfig contains a ReportConfigID to identify theReportConfig, a ReportConfigType to specify the time domain behavior ofthe report (either aperiodic, semi-persistent, or periodic), aReportQuantity to indicate the CSI-related or L1-RSRP-related quantitiesto report, a ReportFreqConfiguration to indicate the reportinggranularity in the frequency domain”. (3GPP TS 38.214 V15.4.0)

To enable the proposed CSC framework, the CSI ReportConfig can bemodified to include:

-   -   an advanced CSI Type II with “compressed explicit channel matrix        feedback”, and/or    -   an advanced CSI Type II with “compressed explicit tx-covariance        matrix feedback”,        in which the UE reports the compressed channel estimates        ƒ_(a)(c₁, c₂, . . . , c_(N)) using PUCCH and/or PUSCH.

If configured for Type-I or Type-II CSI feedback, the ReportConfigcontains a CodebookConfig that specifies configuration parameters forType-1 and Type-II CSI feedback. To enable the proposed CSC framework,the CodebookConfig can be modified to specify the encoding and decodingparameters (a*, b*).

Each ReportConfig contains a ReportFreqConfig:

“ReportFreqConfiguration to indicate the reporting granularity in thefrequency domain. For periodic/semi-persistent reporting, a ReportConfigcontains a ReportSlotConfig to specify the periodicity and slot offset.For aperiodic reporting, a ReportConfig contains anAperiodicReportSlotOffset to specify a set of allowed values of thetiming offset for aperiodic reporting (a particular value is indicatedin DCI). The ReportFreqConfiguration contains parameters to enableconfiguration of at least subband or wideband PMI and CQI reportingseparately. The ReportConfig can also containMeasRestrictionConfig-time-channel to specify parameters to enableconfiguration of time domain measurement restriction for channel. TheReportConfig can also contain MeasRestrictionConfig-time-interference tospecify parameters to enable separate configuration of time domainmeasurement restriction for interference.”

As mentioned above, the encoding and decoding parameters (a*, b*) can betailored specifically to match the reporting variables in theReportFreqConfig.

6.2.5 Updating CSC Parameters

During the cell-association step, a CSC update type can be configured(e.g., as part of the ReportFreqConfig) to determine how CSC parameters(a*, b*) can be updated (if at all) during the UE's connection time tothe BS's cell. Example CSC update types:

-   -   Static: The UE and BS can assume that no update of the decoding        parameters b* will take place. The UE can choose to update        one-or-more of the encoding parameters a* at any time, assuming        such an update is transparent to the BS. As the UE can evaluate        the decoding performance if the decoding parameters b* are        known, it may autonomously update the encoding parameters a*        such that it minimizes the reconstruction error Δ_(d) of the raw        channel estimates to be conveyed in each CSI report.    -   BS-initiated CSC parameter updates: The BS can configure the UE        (e.g., during the above CSI configuration step) to accept        updates to one-or-more CSC parameters. The time-domain behavior        of these updates can be configured to be periodic or aperiodic.    -   UE-initiated CSC parameter updates: The BS can configure the UE        to recommend updates to one-or-more of CSC parameters. The        time-domain behavior of these updates can be configured to be        periodic or aperiodic.    -   Explicit channel estimate updates: The BS can configure the UE        to send raw uplink channel estimate updates from which the BS        can update the CSC parameters. The time-domain behavior of these        updates can be configured to be periodic or aperiodic. Note: As        discussed in the background section, transmitting raw channel        estimates in the CSI feedback report is an expensive process and        should be done infrequently.

6.2.6 CS Compressors and Decompressors Based on an Autoencoder Framework

FIG. 15 shows a compression function 1501 and a decompression function1502 provided in the form of a neural network 1500, according anembodiment. In the present embodiment, compression and decompression isbased on an autoencoder framework.

For simplicity, consider a BS (provided here as an example of a networknode) with N_(TX) antenna ports and a UE (provided here as an example ofa terminal device) with a single antenna port. Suppose that the UE joinsthe cell of the BS, and that the BS configures the UE with a first setof parameters a* and a second set of parameters b*.

The class of encoders

(which act as compression functions) and the decoders

(which act as decompression functions) can be defined by the autoencoderstructure in FIG. 15 .

Let X=[X₁, X₂, . . . , X_(N) _(TX) ]^(T) denote the N_(TX) downlinkchannel estimates 1503 in one frequency interval (e.g., one channelestimate is derived from each configured CSI-RS resource). The firstpart of the encoder ƒ_(a) (in other words, the compression function1501) involves a dimensionality reduction step in which the N_(TX)complex-valued channel measurements X=[X₁, X₂, . . . , X_(N) _(TX) ]^(T)are mapped to N_(CM) complex-valued compressed measurements Z₁, Z₂, . .. , Z_(N) _(CM) . More formally, we haveZ=σ(UX+B)where

-   -   X=[X₁, X₂, . . . , X_(N) _(TX) ]^(T) represents the (N_(TX)×1)        vector of complex CSI-RS measurements taken by the UE. That is,        X        denotes the UE's estimate 1503 of the ≡th CSI-RS resource on its        antenna port. Alternatively, one can make X a (2N_(TX)×1) vector        of real-valued CSI-RS measurements by splitting each complex        measurement into its real and imaginary parts.    -   B=[B₁, B₂, . . . , B_(N) _(CM) ]^(T) denotes the N_(CM) biases        (equivalently represented by the input node 1504 in FIG. 15 ).    -   U is an (N_(CM)×N_(TX))-complex matrix used by the UE, where        N_(CM) represents the number of complex values produced by the        UE when applying the transformation UX+B (this is hence the        dimension of the compressed CSI) where N_(CM)<N_(TX) meaning        that U will constitute a dimension reduction. The elements of U        and the biases B can be specified by a first set of parameters        a* configured by the BS. For example, the first set of        parameters (which corresponds to encoder parameters) can be        a*:={u₁₁, u₁₂, . . . , u_(N) _(CM) _(N) _(TX) , b₁, b₂, . . . ,        b_(N) _(CM) } where u_(ij) denotes the (i,j)-th element of U and        b_(i) denotes the i-th bias term. The matrix U is represented by        the input nodes 1505, the hidden nodes 1506 and the paths 1507        between the nodes. The transformation UX may be regarded as a        linear part 1508 of the compressor 1501. In other words, the        compressor 1501 comprises a first function UX+B which has a        linear portion UX and a constant portion B.    -   A non-linear activation function σ:        →        (indicated by 1509 in FIG. 15 ) is applied to each element 1510        of the (N_(CM)×1)-complex matrix Y=UX+B. Such use of the        activation function constitutes a non-linear part 1511 or        subfunction of the compression function 1501. The        (N_(CM)×1)-complex output Z (indicated by 1512 in FIG. 15 ) of        this non-linear subfunction 1511 is then quantized 1513 to        generate the quantized compressed measurement 1514 that is sent        to the BS over the uplink represented by a finite set of bits.        The activation function σ is a non-linear activation function,        such as sigmoid or ReLu. In this embodiment, we assume that the        sigmoid activation function is used on the real and imaginary        components of each element of Y. The scalar non-linear        activation function 1509 is applied to element by element the        output 1510 of the linear part 1508. The non-linear activation        function 1509 can turn on/off different elements in the output        from linear part 1508 to achieve better compression performance.

After the dimensionality-reduction step, the N_(CM) complex-valuedmeasurements Z are quantized 1513 to finite-discrete values {tilde over(Z)} for transmission over the uplink. The quantization 1513 may includea scalar or vector quantizer. For example, a scalar quantizer 1515 maybe applied to each element of Z.

The embodiment in FIG. 15 uses N_(CM) stochastic scalar quantizers 1515(one for each hidden node 1506), where each stochastic scalar quantizer1515 consists of L levels (here, for example, we have M=L^(N) ^(CM) ).An embodiment and motivation for using “stochastic” scalar quantizers isexplained below. Of course, other embodiments could use standard scalarand/or vector quantization methods together with entropy coding.

The decoder (in other words, the decompression function 1502) can beimplemented in a manner like the encoder (in other words, thecompression function 1501): The quantized message {tilde over (Z)} istransmitted to the BS. The BS multiplies {tilde over (Z)} by an(N_(CM)×N_(TX))-complex matrix V (this corresponds to a linear part 1516or subfunction of the decompression function 1502), and then passes theresult 1517 through a non-linear part 1518 or subfunction of thedecompression function 1502 to obtain the decompressed/reconstructedchannel estimates 1519. The non-linear part 1518 involves application ofa non-linear activation function ρ:

→

(indicated by 1520 in FIG. 15 ) to the respective outputs 1517 of thelinear part 1516. The activation function 1520 employed in thedecompression function 1502 may for example be the same activationfunction 1509 as employed in the compression function 1501, but it couldalso be a different activation function. As in the compression function1501, biases may be applied via use of a bias node 1521. Due to the biasnode 1521, the part 1516 of the decompression function 1502 comprises alinear portion (forming linear combinations of the quantized componentsof the message {tilde over (Z)}) and a constant portion (provided by thebias node 1521). The elements of V and the output biases 1521 arespecified by a second set of parameters b*.

The activation functions 1509 and 1520 at the encoder 1501 and decoder1502 as well as the stochastic scalar quantizer 1515 are all fixed inthis embodiment.

6.2.7 Stochastic Scalar Quantization

The stochastic scalar quantization used in FIG. 15 can be defined asfollows. This embodiment applies the stochastic scalar quantizer 1515 tothe output of a sigmoid function 1509. The range of the sigmoid is theunit interval [0,1], and we have assumed L quantization levels. Wedivide the unit interval into L equal subintervals

$\left\lbrack {\frac{\ell - 1}{L},\frac{\ell}{L}} \right\rbrack,{\ell = 1},2,\ldots,{L.}$

Let z∈[0,1] denote the output of a sigmoid function. If z falls withinthe first or last interval, then the quantizer outputs 1 or Lrespectively. Otherwise, if

$z \in \left\lbrack {\frac{\ell - 1}{L},\frac{\ell}{L}} \right\rbrack$

For some

∈{2, 3, . . . , L−1} then the quantizer outputs:

-   -   (        −1) with probability (z−        +1)L and    -   with probability (        −z)L.

This stochastic quantizer can be viewed as a type of regularization.

6.2.8 CSC Parameter Space

The encoder parameter space

and the decoder space

collectively define the set of all (allowable) encoder weights, encoderbiases, decoder weights, and decoder biases.

6.2.9 Initial CSC Parameters

The CSC parameters a* and b* are determined by the BS and uniquelyspecify the initial encoder/decoder weights and biases from theparameter spaces

and

. For example, the initial CSC parameters a* and b* can be designed andupdated by the BS in an offline manner using supervised-learningtechniques and historical channel data.

6.2.10 Updating CSC Parameters

The encoder/decoder weights and encoder/decoder biases can be updated asdescribed in Section 6.2.5 above.

The UEs' channel measurements without normalization can differ byseveral orders of magnitude (in linear scale). To partially combat thisdramatic variation, we have normalized the CSI-RS measurements by thecorresponding (wideband) L1-RSRP. The L1-RSRP is measured by the UE foreach CSI report and may in some embodiments be reported to the BS. Thus,the normalized values can be used for both training theautoencoder-based CSC and during live operation.

Normalizing the CSI-RS measurements allows, for example, the resultingtrained autoencoder-based CSC to be used for several different UEs withdifferent pathlosses. Without normalization, one would have to performspecific training for many feasible pathlosses, which might not bepractical.

It is well documented in the machine-learning literature thatgradient-decent based backpropagation algorithms require normalizedtraining data. Using RSRP in the above way will help such algorithmsconverge.

As described above, the autoencoder-base CSC can be trained usinggradient-decent type algorithms with backpropagation. In suchalgorithms, the derivative of any scalar (or, vector) quantizer must beundefined at some point. In such embodiments, we can use the gradient ofthe expectation of the stochastic scalar quantizer, which is continuous.

6.3 Further Example Embodiments

The following example embodiments may be envisaged in addition toexample embodiment 1 described above in section 5.7.

Embodiment 2: Consider the system described above in exampleembodiment 1. The autoencoder illustrated in FIG. 15 can be replaced byany other function whose parameters can be trained using historical dataand a fixed cost function. For example, a (deep) convolutional neuralnetwork, an LSTM network, a neural Turing machine, or a perceptron, canall be used.

Embodiment 3: Consider the system described in example embodiment 1. Theinput channel estimates X=[X₁, X₂, . . . , X_(N) _(TX) ]^(T) can benormalized by the corresponding L1-RSRP. The motivation for such anormalization could be to simplify the training of the CSC encoder anddecoder. Since the L1-RSRP is known to both the BS and UE, the channelestimates can be normalized and denormalized before and aftercompression.

Embodiment 4: Consider the system described in example embodiment 1. Theinput channel estimates X=[X₁, X₂, . . . , X_(N) _(TX) ]^(T) can remainunnormalized, and additional “L1-RSRP” input(s) are provided to the CSC.For example, the CSC encoder parameters a include the L1-RSRP.

Embodiment 5: Consider the system described in example embodiment 1. Theinput channel estimates X=[X₁, X₂, . . . , X_(N) _(TX) ]^(T) canrepresent the estimated channels across a given frequency interval basedon the measured CSI-RS.

Embodiment 6: Consider the system described in example embodiment 1. Theinput channel estimates X=[X₁, X₂, . . . , X_(KN) _(TX) ]^(T) (i.e, thesize of X is (KN_(TX)×1)), and it constitutes K concatenatedmeasurements/channel estimates for multiple time and/or frequencyintervals.

Embodiment 7: Consider the system described in example embodiment 1. Thelinear part of the encoder can be of the form U=U₁U₂ where U₁ can beshared by many UEs (e.g., in a site or network specific manner) and U₂can be configured specifically for the UE. Indeed, the embodiment maygeneralize the network to have L_(enc) encoding layers and L_(dec)encoding layers with possibly different number of nodes and differentactivation functions at each layer. The parameters for a given layer canbe shared between one or more UEs. In one such embodiment, the U₁ partof the encoder is provided as a cell-specific configuration by initialRRC configuration and remains static for the entirety of the UEconnection time while the U₂ part of the parameters is continuouslyupdated by feedback of training data to and/or from the UE.

Embodiment 8: Consider the system described in example embodiment 1. TheUE may change the configured CS compressor ƒ_(a*):

^(N)→{1, 2, . . . , M} to a different compressor ƒ_(ã):

^(N)→{1, 2, . . . , M} if it determines that the new CSI compressorƒ_(ã) achieves better distortion (see section 6.2.2). It can measure theperformance using the signaled cost function.

Embodiment 9: Consider the system described in example embodiment 1. TheBS can choose to update the UE's CSC parameters using, for example, theUE's CSI report (e.g., the rank indicator (RI), channel qualityindicator (CQI) or L1-RSRP) or other related variables. For example, theBS can modify the CSC parameters to better match the number ofconfigured CSI-RS ports, the transmission rank, and the channelconditions.

Embodiment 10: Consider the system described in example embodiment 1. Insome embodiments the matrices U and/or V may originate from a codebook,hence a limited set of matrices U and/or V are pre-specified, and eachmatrix is represented by an index in the codebook. By using this index,the gNB and UE may thus exchange information about which U and/or V tobe used. Here training may be performed by first relaxing U and V to acontinuous parameter space, training (by, for example stochasticgradient decent), and then choosing the closed U and V in the codebookto the trained solution. In another embodiment U and/or V are insteadparameterized by a set of parameters and functions; thus, by exchangingthese parameters U and/or V are implicitly exchanged.

Embodiment 11: Consider the system described in example embodiment 1.The cost function and CS compressor and decompressor may be optimizedwith respect to the expected UE throughput, the system cell edgethroughput or some other metric relating to system or UE throughput.

Embodiment 12: Consider the system described in example embodiment 1.This optimization may in one embodiment be a continuous or periodicprocess such that (a*, b*) will be updated when new measurements X[T+1]are obtained. In another embodiment it is rather so that theoptimization is performed in an initial phase, based on for exampleX[1], X[2], . . . , X[T], and the set of matrices U and V are then keptfixed. The optimization may be performed in a UE specific way, a sectorspecific way, a site specific way or a network specific way.

Embodiment 13: Consider the system described in example embodiment 1. Inanother embodiment the BS measures the channel using reciprocity (e.g.,from uplink SRS) and has access to both the CS compressor anddecompressor. It may thus perform an optimization of these and signal tothe UE which a* (i.e., elements of U) to be used. In one such embodimentthe UE does not need to have access to the decompressor.

Embodiment 14: Consider the system described in example embodiment 1.Additionally, the UE reports a preferred transmission rank (RI) as partof the CSI report and instead of using the channel coefficients as inputto the encoder, the coefficients of the RI principal eigenvectors areused as input such that X=[X₁, X₂, . . . , X_(RI·N) _(TX) ]^(T)

Embodiment 15: Consider the system described in example embodiment 6.Before inputting the channel estimates to the encoder, a transformation(such as a DFT or a DCT) is applied to the frequency domain channel soas to transform it to delay domain in order to encode the delay domainchannel coefficients.

Embodiment 16: Consider the system described in example embodiment 1,with autoencoder replaced by a much more sophisticated network (forexample, a deep convolutions neural network with, for example, manymillions of parameters). Instead of requiring all UEs to use the samecomplex deep convolutional neural network, the BS can design one-or-moreapproximations to complex network tailored for different UEs (e.g., UEtypes, scheduling configurations etc). In this way, the complexityrequirements of the CS compressor can be tailored to the UE capabilitiesand scheduling requirements.

7. Embodiments of Terminal Devices, Network Nodes, Computer ProgramsEtc.

The methods of operating a terminal device, described above withreference to FIGS. 1-15 and 17 , represent a first aspect of the presentdisclosure. FIG. 16 shows a wireless network, and will be furtherdescribed in the next section. The wireless devices 502, 502 b and 502 cdescribed below with reference to FIG. 16 are examples of terminaldevices, and represent a third aspect of the present disclosure. Theterminal device 502 (or the processing circuitry 1620 of the terminaldevice 502) may for example be configured to perform the method of anyof the embodiments of the first aspect described above. The terminaldevice 502 (or the processing circuitry 1620 of the terminal device 502)may for example be configured to perform the method 300 described abovewith reference to FIG. 3 .

According to an embodiment, the terminal device 502 may compriseprocessing circuitry 1620 and a memory 1630 (or a device-readablemedium) containing instructions executable by the processing circuitry1620 whereby the terminal device 502 is operable to perform the methodof any of the embodiments of the first aspect described above.

It will be appreciated that a non-transitory computer-readable medium,such as for example the device-readable medium 1630, may storeinstructions which, when executed by a computer (or by processingcircuitry such as 1620), cause the computer (or the processing circuitry1620) to perform the method of any of the embodiments of the firstaspect described above. It will also be appreciated that anon-transitory computer-readable medium 1630 storing such instructionsneed not necessarily be comprised in a terminal device 502. On thecontrary, such a non-transitory computer-readable medium 1630 could beprovided on its own, for example at a location remote from the terminaldevice 502.

It will be appreciated that the terminal device 502 need not necessarilycomprise all those components described below with reference to FIG. 16. For a terminal device 502 according to an embodiment of the thirdaspect, it is sufficient that the terminal device 502 comprises meansfor performing the steps of the method of the corresponding embodimentof the first aspect.

Similarly, it will be appreciated that the processing circuitry 1620need not necessarily comprise all those components described below withreference to FIG. 16 .

The methods of operating a network node, described above with referenceto FIGS. 1-15 and 17 , represent a second aspect of the presentdisclosure. The network nodes 501 and 501 b described below withreference to FIG. 16 represent a fourth aspect of the presentdisclosure. The network node 501 (or the processing circuitry 1670 ofthe network node 501) may for example be configured to perform themethod of any of the embodiments of the second aspect described above.The network node 501 (or the processing circuitry 1670 of the networknode 501) may for example be configured to perform the method 400described above with reference to FIG. 4 .

According to an embodiment, the network node 501 may comprise processingcircuitry 1670 and a memory 1680 (or a device-readable medium)containing instructions executable by the processing circuitry 1670whereby the network node 501 is operable to perform the method of any ofthe embodiments of the second aspect described above.

It will be appreciated that a non-transitory computer-readable medium,such as for example the device-readable medium 1680, may storeinstructions which, when executed by a computer (or by processingcircuitry such as 1670), cause the computer (or the processing circuitry1670) to perform the method of any of the embodiments of the secondaspect described above. It will also be appreciated that anon-transitory computer-readable medium 1680 storing such instructionsneed not necessarily be comprised in a network node 501. On thecontrary, such a non-transitory computer-readable medium 1680 could beprovided on its own, for example at a location remote from the networknode 501.

It will be appreciated that the network node 501 need not necessarilycomprise all those components described below with reference to FIG. 16. For a network node 501 according to an embodiment of the fourthaspect, it is sufficient that the network node 501 comprises means forperforming the steps of the method of the corresponding embodiment ofthe second aspect.

Similarly, it will be appreciated that the processing circuitry 1670need not necessarily comprise all those components described below withreference to FIG. 16 .

8. Overview of a Wireless Network and Parts Thereof

FIG. 16 shows a wireless network in accordance with some embodiments.Although the subject matter described herein may be implemented in anyappropriate type of system using any suitable components, theembodiments disclosed herein are described in relation to a wirelessnetwork, such as the example wireless network illustrated in FIG. 16 .For simplicity, the wireless network of FIG. 16 only depicts network1606, network nodes 501 and 501 b, and WDs 502, 502 b, and 502 c. Inpractice, a wireless network may further include any additional elementssuitable to support communication between wireless devices or between awireless device and another communication device, such as a landlinetelephone, a service provider, or any other network node or end device.Of the illustrated components, network node 501 and wireless device (WD)502 are depicted with additional detail. The wireless network mayprovide communication and other types of services to one or morewireless devices to facilitate the wireless devices' access to and/oruse of the services provided by, or via, the wireless network.

The wireless network may comprise and/or interface with any type ofcommunication, telecommunication, data, cellular, and/or radio networkor other similar type of system. In some embodiments, the wirelessnetwork may be configured to operate according to specific standards orother types of predefined rules or procedures. Thus, particularembodiments of the wireless network may implement communicationstandards, such as Global System for Mobile Communications (GSM),Universal Mobile Telecommunications System (UMTS), Long Term Evolution(LTE), and/or other suitable 2G, 3G, 4G, or 5G standards; wireless localarea network (WLAN) standards, such as the IEEE 802.11 standards; and/orany other appropriate wireless communication standard, such as theWorldwide Interoperability for Microwave Access (WiMax), Bluetooth,Z-Wave and/or ZigBee standards.

Network 1606 may comprise one or more backhaul networks, core networks,IP networks, public switched telephone networks (PSTNs), packet datanetworks, optical networks, wide-area networks (WANs), local areanetworks (LANs), wireless local area networks (WLANs), wired networks,wireless networks, metropolitan area networks, and other networks toenable communication between devices.

Network node 501 and WD 502 comprise various components described inmore detail below. These components work together in order to providenetwork node and/or wireless device functionality, such as providingwireless connections in a wireless network. In different embodiments,the wireless network may comprise any number of wired or wirelessnetworks, network nodes, base stations, controllers, wireless devices,relay stations, and/or any other components or systems that mayfacilitate or participate in the communication of data and/or signalswhether via wired or wireless connections.

As used herein, network node refers to equipment capable, configured,arranged and/or operable to communicate directly or indirectly with awireless device and/or with other network nodes or equipment in thewireless network to enable and/or provide wireless access to thewireless device and/or to perform other functions (e.g., administration)in the wireless network. Examples of network nodes include, but are notlimited to, access points (APs) (e.g., radio access points), basestations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs(eNBs) and NR NodeBs (gNBs)). Base stations may be categorized based onthe amount of coverage they provide (or, stated differently, theirtransmit power level) and may then also be referred to as femto basestations, pico base stations, micro base stations, or macro basestations. A base station may be a relay node or a relay donor nodecontrolling a relay. A network node may also include one or more (orall) parts of a distributed radio base station such as centralizeddigital units and/or remote radio units (RRUs), sometimes referred to asRemote Radio Heads (RRHs). Such remote radio units may or may not beintegrated with an antenna as an antenna integrated radio. Parts of adistributed radio base station may also be referred to as nodes in adistributed antenna system (DAS). Yet further examples of network nodesinclude multi-standard radio (MSR) equipment such as MSR BSs, networkcontrollers such as radio network controllers (RNCs) or base stationcontrollers (BSCs), base transceiver stations (BTSs), transmissionpoints, transmission nodes, multi-cell/multicast coordination entities(MCEs), core network nodes (e.g., MSCs, MMEs), O&M nodes, OSS nodes, SONnodes, positioning nodes (e.g., E-SMLCs), and/or MDTs. As anotherexample, a network node may be a virtual network node. More generally,however, network nodes may represent any suitable device (or group ofdevices) capable, configured, arranged, and/or operable to enable and/orprovide a wireless device with access to the wireless network or toprovide some service to a wireless device that has accessed the wirelessnetwork.

In FIG. 16 , network node 501 includes processing circuitry 1670, devicereadable medium 1680, interface 1690, auxiliary equipment 1684, powersource 1686, power circuitry 1687, and antenna 1662. Although networknode 501 illustrated in the example wireless network of FIG. 16 mayrepresent a device that includes the illustrated combination of hardwarecomponents, other embodiments may comprise network nodes with differentcombinations of components. It is to be understood that a network nodecomprises any suitable combination of hardware and/or software needed toperform the tasks, features, functions and methods disclosed herein.Moreover, while the components of network node 501 are depicted assingle boxes located within a larger box, or nested within multipleboxes, in practice, a network node may comprise multiple differentphysical components that make up a single illustrated component (e.g.,device readable medium 1680 may comprise multiple separate hard drivesas well as multiple RAM modules).

Similarly, network node 501 may be composed of multiple physicallyseparate components (e.g., a NodeB component and a RNC component, or aBTS component and a BSC component, etc.), which may each have their ownrespective components. In certain scenarios in which network node 501comprises multiple separate components (e.g., BTS and BSC components),one or more of the separate components may be shared among severalnetwork nodes. For example, a single RNC may control multiple NodeB's.In such a scenario, each unique NodeB and RNC pair, may in someinstances be considered a single separate network node. In someembodiments, network node 501 may be configured to support multipleradio access technologies (RATs). In such embodiments, some componentsmay be duplicated (e.g., separate device readable medium 1680 for thedifferent RATs) and some components may be reused (e.g., the sameantenna 1662 may be shared by the RATs). Network node 501 may alsoinclude multiple sets of the various illustrated components fordifferent wireless technologies integrated into network node 501, suchas, for example, GSM, WCDMA, LTE, NR, WiFi, or Bluetooth wirelesstechnologies. These wireless technologies may be integrated into thesame or different chip or set of chips and other components withinnetwork node 501.

Processing circuitry 1670 is configured to perform any determining,calculating, or similar operations (e.g., certain obtaining operations)described herein as being provided by a network node. These operationsperformed by processing circuitry 1670 may include processinginformation obtained by processing circuitry 1670 by, for example,converting the obtained information into other information, comparingthe obtained information or converted information to information storedin the network node, and/or performing one or more operations based onthe obtained information or converted information, and as a result ofsaid processing making a determination.

Processing circuitry 1670 may comprise a combination of one or more of amicroprocessor, controller, microcontroller, central processing unit,digital signal processor, application-specific integrated circuit, fieldprogrammable gate array, or any other suitable computing device,resource, or combination of hardware, software and/or encoded logicoperable to provide, either alone or in conjunction with other networknode 501 components, such as device readable medium 1680, network node501 functionality. For example, processing circuitry 1670 may executeinstructions stored in device readable medium 1680 or in memory withinprocessing circuitry 1670. Such functionality may include providing anyof the various wireless features, functions, or benefits discussedherein. In some embodiments, processing circuitry 1670 may include asystem on a chip (SOC).

In some embodiments, processing circuitry 1670 may include one or moreof radio frequency (RF) transceiver circuitry 1672 and basebandprocessing circuitry 1674. In some embodiments, radio frequency (RF)transceiver circuitry 1672 and baseband processing circuitry 1674 may beon separate chips (or sets of chips), boards, or units, such as radiounits and digital units. In alternative embodiments, part or all of RFtransceiver circuitry 1672 and baseband processing circuitry 1674 may beon the same chip or set of chips, boards, or units

In certain embodiments, some or all of the functionality describedherein as being provided by a network node, base station, eNB or othersuch network device may be performed by processing circuitry 1670executing instructions stored on device readable medium 1680 or memorywithin processing circuitry 1670. In alternative embodiments, some orall of the functionality may be provided by processing circuitry 1670without executing instructions stored on a separate or discrete devicereadable medium, such as in a hard-wired manner. In any of thoseembodiments, whether executing instructions stored on a device readablestorage medium or not, processing circuitry 1670 can be configured toperform the described functionality. The benefits provided by suchfunctionality are not limited to processing circuitry 1670 alone or toother components of network node 501, but are enjoyed by network node501 as a whole, and/or by end users and the wireless network generally.

Device readable medium 1680 may comprise any form of volatile ornon-volatile computer readable memory including, without limitation,persistent storage, solid-state memory, remotely mounted memory,magnetic media, optical media, random access memory (RAM), read-onlymemory (ROM), mass storage media (for example, a hard disk), removablestorage media (for example, a flash drive, a Compact Disk (CD) or aDigital Video Disk (DVD)), and/or any other volatile or non-volatile,non-transitory device readable and/or computer-executable memory devicesthat store information, data, and/or instructions that may be used byprocessing circuitry 1670. Device readable medium 1680 may store anysuitable instructions, data or information, including a computerprogram, software, an application including one or more of logic, rules,code, tables, etc. and/or other instructions capable of being executedby processing circuitry 1670 and, utilized by network node 501. Devicereadable medium 1680 may be used to store any calculations made byprocessing circuitry 1670 and/or any data received via interface 1690.In some embodiments, processing circuitry 1670 and device readablemedium 1680 may be considered to be integrated.

Interface 1690 is used in the wired or wireless communication ofsignalling and/or data between network node 501, network 1606, and/orWDs 502. As illustrated, interface 1690 comprises port(s)/terminal(s)1694 to send and receive data, for example to and from network 1606 overa wired connection. Interface 1690 also includes radio front endcircuitry 1692 that may be coupled to, or in certain embodiments a partof, antenna 1662. Radio front end circuitry 1692 comprises filters 1698and amplifiers 1696. Radio front end circuitry 1692 may be connected toantenna 1662 and processing circuitry 1670. Radio front end circuitrymay be configured to condition signals communicated between antenna 1662and processing circuitry 1670. Radio front end circuitry 1692 mayreceive digital data that is to be sent out to other network nodes orWDs via a wireless connection. Radio front end circuitry 1692 mayconvert the digital data into a radio signal having the appropriatechannel and bandwidth parameters using a combination of filters 1698and/or amplifiers 1696. The radio signal may then be transmitted viaantenna 1662. Similarly, when receiving data, antenna 1662 may collectradio signals which are then converted into digital data by radio frontend circuitry 1692. The digital data may be passed to processingcircuitry 1670. In other embodiments, the interface may comprisedifferent components and/or different combinations of components.

In certain alternative embodiments, network node 501 may not includeseparate radio front end circuitry 1692, instead, processing circuitry1670 may comprise radio front end circuitry and may be connected toantenna 1662 without separate radio front end circuitry 1692. Similarly,in some embodiments, all or some of RF transceiver circuitry 1672 may beconsidered a part of interface 1690. In still other embodiments,interface 1690 may include one or more ports or terminals 1694, radiofront end circuitry 1692, and RF transceiver circuitry 1672, as part ofa radio unit (not shown), and interface 1690 may communicate withbaseband processing circuitry 1674, which is part of a digital unit (notshown).

Antenna 1662 may include one or more antennas, or antenna arrays,configured to send and/or receive wireless signals. Antenna 1662 may becoupled to radio front end circuitry 1690 and may be any type of antennacapable of transmitting and receiving data and/or signals wirelessly. Insome embodiments, antenna 1662 may comprise one or moreomni-directional, sector or panel antennas operable to transmit/receiveradio signals between, for example, 2 GHz and 66 GHz. Anomni-directional antenna may be used to transmit/receive radio signalsin any direction, a sector antenna may be used to transmit/receive radiosignals from devices within a particular area, and a panel antenna maybe a line of sight antenna used to transmit/receive radio signals in arelatively straight line. In some instances, the use of more than oneantenna may be referred to as MIMO. In certain embodiments, antenna 1662may be separate from network node 501 and may be connectable to networknode 501 through an interface or port.

Antenna 1662, interface 1690, and/or processing circuitry 1670 may beconfigured to perform any receiving operations and/or certain obtainingoperations described herein as being performed by a network node. Anyinformation, data and/or signals may be received from a wireless device,another network node and/or any other network equipment. Similarly,antenna 1662, interface 1690, and/or processing circuitry 1670 may beconfigured to perform any transmitting operations described herein asbeing performed by a network node. Any information, data and/or signalsmay be transmitted to a wireless device, another network node and/or anyother network equipment.

Power circuitry 1687 may comprise, or be coupled to, power managementcircuitry and is configured to supply the components of network node 501with power for performing the functionality described herein. Powercircuitry 1687 may receive power from power source 1686. Power source1686 and/or power circuitry 1687 may be configured to provide power tothe various components of network node 501 in a form suitable for therespective components (e.g., at a voltage and current level needed foreach respective component). Power source 1686 may either be included inor external to, power circuitry 1687 and/or network node 501. Forexample, network node 501 may be connectable to an external power source(e.g., an electricity outlet) via an input circuitry or interface suchas an electrical cable, whereby the external power source supplies powerto power circuitry 1687. As a further example, power source 1686 maycomprise a source of power in the form of a battery or battery packwhich is connected to, or integrated in, power circuitry 1687. Thebattery may provide backup power should the external power source fail.Other types of power sources, such as photovoltaic devices, may also beused.

Alternative embodiments of network node 501 may include additionalcomponents beyond those shown in FIG. 16 that may be responsible forproviding certain aspects of the network node's functionality, includingany of the functionality described herein and/or any functionalitynecessary to support the subject matter described herein. For example,network node 501 may include user interface equipment to allow input ofinformation into network node 501 and to allow output of informationfrom network node 501. This may allow a user to perform diagnostic,maintenance, repair, and other administrative functions for network node501.

As used herein, wireless device (WD) refers to a device capable,configured, arranged and/or operable to communicate wirelessly withnetwork nodes and/or other wireless devices. Unless otherwise noted, theterm WD may be used interchangeably herein with user equipment (UE) orterminal device. Communicating wirelessly may involve transmittingand/or receiving wireless signals using electromagnetic waves, radiowaves, infrared waves, and/or other types of signals suitable forconveying information through air. In some embodiments, a WD may beconfigured to transmit and/or receive information without direct humaninteraction. For instance, a WD may be designed to transmit informationto a network on a predetermined schedule, when triggered by an internalor external event, or in response to requests from the network. Examplesof a WD include, but are not limited to, a smart phone, a mobile phone,a cell phone, a voice over IP (VoIP) phone, a wireless local loop phone,a desktop computer, a personal digital assistant (PDA), a wirelesscameras, a gaming console or device, a music storage device, a playbackappliance, a wearable terminal device, a wireless endpoint, a mobilestation, a tablet, a laptop, a laptop-embedded equipment (LEE), alaptop-mounted equipment (LME), a smart device, a wirelesscustomer-premise equipment (CPE). a vehicle-mounted wireless terminaldevice, etc. A WD may support device-to-device (D2D) communication, forexample by implementing a 3GPP standard for sidelink communication,vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I),vehicle-to-everything (V2X) and may in this case be referred to as a D2Dcommunication device. As yet another specific example, in an Internet ofThings (IoT) scenario, a WD may represent a machine or other device thatperforms monitoring and/or measurements, and transmits the results ofsuch monitoring and/or measurements to another WD and/or a network node.The WD may in this case be a machine-to-machine (M2M) device, which mayin a 3GPP context be referred to as an MTC device. As one particularexample, the WD may be a UE implementing the 3GPP narrow band internetof things (NB-IoT) standard. Particular examples of such machines ordevices are sensors, metering devices such as power meters, industrialmachinery, or home or personal appliances (e.g. refrigerators,televisions, etc.) personal wearables (e.g., watches, fitness trackers,etc.). In other scenarios, a WD may represent a vehicle or otherequipment that is capable of monitoring and/or reporting on itsoperational status or other functions associated with its operation. AWD as described above may represent the endpoint of a wirelessconnection, in which case the device may be referred to as a wirelessterminal. Furthermore, a WD as described above may be mobile, in whichcase it may also be referred to as a mobile device or a mobile terminal.

As illustrated, wireless device 502 includes antenna 1611, interface1614, processing circuitry 1620, device readable medium 1630, userinterface equipment 1632, auxiliary equipment 1634, power source 1636and power circuitry 1637. WD 502 may include multiple sets of one ormore of the illustrated components for different wireless technologiessupported by WD 502, such as, for example, GSM, WCDMA, LTE, NR, WiFi,WiMAX, or Bluetooth wireless technologies, just to mention a few. Thesewireless technologies may be integrated into the same or different chipsor set of chips as other components within WD 502.

Antenna 1611 may include one or more antennas or antenna arrays,configured to send and/or receive wireless signals, and is connected tointerface 1614. In certain alternative embodiments, antenna 1611 may beseparate from WD 502 and be connectable to WD 502 through an interfaceor port. Antenna 1611, interface 1614, and/or processing circuitry 1620may be configured to perform any receiving or transmitting operationsdescribed herein as being performed by a WD. Any information, dataand/or signals may be received from a network node and/or another WD. Insome embodiments, radio front end circuitry and/or antenna 1611 may beconsidered an interface.

As illustrated, interface 1614 comprises radio front end circuitry 1612and antenna 1611. Radio front end circuitry 1612 comprise one or morefilters 1618 and amplifiers 1616. Radio front end circuitry 1614 isconnected to antenna 1611 and processing circuitry 1620, and isconfigured to condition signals communicated between antenna 1611 andprocessing circuitry 1620. Radio front end circuitry 1612 may be coupledto or a part of antenna 1611. In some embodiments, WD 502 may notinclude separate radio front end circuitry 1612; rather, processingcircuitry 1620 may comprise radio front end circuitry and may beconnected to antenna 1611. Similarly, in some embodiments, some or allof RF transceiver circuitry 1622 may be considered a part of interface1614. Radio front end circuitry 1612 may receive digital data that is tobe sent out to other network nodes or WDs via a wireless connection.Radio front end circuitry 1612 may convert the digital data into a radiosignal having the appropriate channel and bandwidth parameters using acombination of filters 1618 and/or amplifiers 1616. The radio signal maythen be transmitted via antenna 1611. Similarly, when receiving data,antenna 1611 may collect radio signals which are then converted intodigital data by radio front end circuitry 1612. The digital data may bepassed to processing circuitry 1620. In other embodiments, the interfacemay comprise different components and/or different combinations ofcomponents.

Processing circuitry 1620 may comprise a combination of one or more of amicroprocessor, controller, microcontroller, central processing unit,digital signal processor, application-specific integrated circuit, fieldprogrammable gate array, or any other suitable computing device,resource, or combination of hardware, software, and/or encoded logicoperable to provide, either alone or in conjunction with other WD 502components, such as device readable medium 1630, WD 502 functionality.Such functionality may include providing any of the various wirelessfeatures or benefits discussed herein. For example, processing circuitry1620 may execute instructions stored in device readable medium 1630 orin memory within processing circuitry 1620 to provide the functionalitydisclosed herein.

As illustrated, processing circuitry 1620 includes one or more of RFtransceiver circuitry 1622, baseband processing circuitry 1624, andapplication processing circuitry 1626. In other embodiments, theprocessing circuitry may comprise different components and/or differentcombinations of components. In certain embodiments processing circuitry1620 of WD 502 may comprise a SOC. In some embodiments, RF transceivercircuitry 1622, baseband processing circuitry 1624, and applicationprocessing circuitry 1626 may be on separate chips or sets of chips. Inalternative embodiments, part or all of baseband processing circuitry1624 and application processing circuitry 1626 may be combined into onechip or set of chips, and RF transceiver circuitry 1622 may be on aseparate chip or set of chips. In still alternative embodiments, part orall of RF transceiver circuitry 1622 and baseband processing circuitry1624 may be on the same chip or set of chips, and application processingcircuitry 1626 may be on a separate chip or set of chips. In yet otheralternative embodiments, part or all of RF transceiver circuitry 1622,baseband processing circuitry 1624, and application processing circuitry1626 may be combined in the same chip or set of chips. In someembodiments, RF transceiver circuitry 1622 may be a part of interface1614. RF transceiver circuitry 1622 may condition RF signals forprocessing circuitry 1620.

In certain embodiments, some or all of the functionality describedherein as being performed by a WD may be provided by processingcircuitry 1620 executing instructions stored on device readable medium1630, which in certain embodiments may be a computer-readable storagemedium. In alternative embodiments, some or all of the functionality maybe provided by processing circuitry 1620 without executing instructionsstored on a separate or discrete device readable storage medium, such asin a hard-wired manner. In any of those particular embodiments, whetherexecuting instructions stored on a device readable storage medium ornot, processing circuitry 1620 can be configured to perform thedescribed functionality. The benefits provided by such functionality arenot limited to processing circuitry 1620 alone or to other components ofWD 502, but are enjoyed by WD 502 as a whole, and/or by end users andthe wireless network generally.

Processing circuitry 1620 may be configured to perform any determining,calculating, or similar operations (e.g., certain obtaining operations)described herein as being performed by a WD. These operations, asperformed by processing circuitry 1620, may include processinginformation obtained by processing circuitry 1620 by, for example,converting the obtained information into other information, comparingthe obtained information or converted information to information storedby WD 502, and/or performing one or more operations based on theobtained information or converted information, and as a result of saidprocessing making a determination.

Device readable medium 1630 may be operable to store a computer program,software, an application including one or more of logic, rules, code,tables, etc. and/or other instructions capable of being executed byprocessing circuitry 1620. Device readable medium 1630 may includecomputer memory (e.g., Random Access Memory (RAM) or Read Only Memory(ROM)), mass storage media (e.g., a hard disk), removable storage media(e.g., a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or anyother volatile or non-volatile, non-transitory device readable and/orcomputer executable memory devices that store information, data, and/orinstructions that may be used by processing circuitry 1620. In someembodiments, processing circuitry 1620 and device readable medium 1630may be considered to be integrated.

User interface equipment 1632 may provide components that allow for ahuman user to interact with WD 502. Such interaction may be of manyforms, such as visual, audial, tactile, etc. User interface equipment1632 may be operable to produce output to the user and to allow the userto provide input to WD 502. The type of interaction may vary dependingon the type of user interface equipment 1632 installed in WD 502. Forexample, if WD 502 is a smart phone, the interaction may be via a touchscreen; if WD 502 is a smart meter, the interaction may be through ascreen that provides usage (e.g., the number of gallons used) or aspeaker that provides an audible alert (e.g., if smoke is detected).User interface equipment 1632 may include input interfaces, devices andcircuits, and output interfaces, devices and circuits. User interfaceequipment 1632 is configured to allow input of information into WD 502,and is connected to processing circuitry 1620 to allow processingcircuitry 1620 to process the input information. User interfaceequipment 1632 may include, for example, a microphone, a proximity orother sensor, keys/buttons, a touch display, one or more cameras, a USBport, or other input circuitry. User interface equipment 1632 is alsoconfigured to allow output of information from WD 502, and to allowprocessing circuitry 1620 to output information from WD 502. Userinterface equipment 1632 may include, for example, a speaker, a display,vibrating circuitry, a USB port, a headphone interface, or other outputcircuitry. Using one or more input and output interfaces, devices, andcircuits, of user interface equipment 1632, WD 502 may communicate withend users and/or the wireless network, and allow them to benefit fromthe functionality described herein.

Auxiliary equipment 1634 is operable to provide more specificfunctionality which may not be generally performed by WDs. This maycomprise specialized sensors for doing measurements for variouspurposes, interfaces for additional types of communication such as wiredcommunications etc. The inclusion and type of components of auxiliaryequipment 1634 may vary depending on the embodiment and/or scenario.

Power source 1636 may, in some embodiments, be in the form of a batteryor battery pack. Other types of power sources, such as an external powersource (e.g., an electricity outlet), photovoltaic devices or powercells, may also be used. WD 502 may further comprise power circuitry1637 for delivering power from power source 1636 to the various parts ofWD 502 which need power from power source 1636 to carry out anyfunctionality described or indicated herein. Power circuitry 1637 may incertain embodiments comprise power management circuitry. Power circuitry1637 may additionally or alternatively be operable to receive power froman external power source; in which case WD 502 may be connectable to theexternal power source (such as an electricity outlet) via inputcircuitry or an interface such as an electrical power cable. Powercircuitry 1637 may also in certain embodiments be operable to deliverpower from an external power source to power source 1636. This may be,for example, for the charging of power source 1636. Power circuitry 1637may perform any formatting, converting, or other modification to thepower from power source 1636 to make the power suitable for therespective components of WD 502 to which power is supplied.

9. Miscellaneous

The person skilled in the art realizes that the proposed approachpresented in the present disclosure is by no means limited to thepreferred embodiments described above. On the contrary, manymodifications and variations are possible. For example, the methodsdescribed above with reference to FIGS. 3-15 may be combined to formfurther embodiments. Further, it will be appreciated that the terminaldevice 502 and the network node 501 shown in FIG. 16 are merely intendedas examples, and that other terminal devices and network nodes may alsoperform the methods described above with reference to FIGS. 3-15 . Itwill also be appreciated that the method steps described with referenceto FIGS. 7-11 need not necessarily be performed in the specific ordershown in these figures, unless otherwise indicated.

The system 1500 described above with reference to FIG. 15 is intended asa simple example to illustrate the proposed compression concept. It willbe appreciated that a larger system (for example using additional layersof nodes) may provide better performance.

It will be appreciated that the term radio access technology, or RAT,employed herein may refer to any RAT e.g. UTRA, E-UTRA, narrow bandinternet of things (NB-IoT), WiFi, Bluetooth, next generation RAT (NR),4G, 5G, etc. The network nodes and terminal devices described herein maybe capable of supporting a single or multiple RATs.

Additionally, variations to the disclosed embodiments can be understoodand effected by those skilled in the art. It will be appreciated thatthe word “comprising” does not exclude other elements or steps, and thatthe indefinite article “a” or “an” does not exclude a plurality. Theword “or” is not to be interpreted as an exclusive or (sometimesreferred to as “XOR”). On the contrary, expressions such as “A or B”covers all the cases “A and not B”, “B and not A” and “A and B”. Themere fact that certain measures are recited in mutually differentdependent embodiments does not indicate that a combination of thesemeasures cannot be used to advantage.

REFERENCES

-   Candes, E., Romberg, J., & Tao, T. (2006). Robust uncertainty    principles: Exact signal reconstruction from highly incomplete    frequency information. IEEE Trans Information Theory, 52(2).-   Ericsson. (2018). IMT2020 self-evaluations: On eMBB spectral    efficiency with NR. Sanya China: 3GPP R1-1805228.-   Kuo, P.-H., Kung, H. T., & Ting, P.-A. (2012). Compressive sensing    based channel feedback protocols for spatially-correlated massive    antenna arrays. IEEE Wireless Communication Network Conf, (pp.    492-497).

What is claimed is:
 1. A method of operating a user equipment (UE), the method comprising: receiving a first set of parameters from a network node; compressing downlink channel estimates using a compression function; and transmitting the compressed downlink channel estimates, wherein the compression function comprises a neural network having: a linear function; a non-linear function; and a quantizer, wherein the linear function is based on at least one of the parameters from the first set of parameters, wherein the linear function is configured to: receive input data; and provide output data with a lower dimension than the input data, wherein the non-linear function is configured to apply a non-linear activation function to each of a plurality of numbers in the output data of the linear function, wherein the quantizer is configured to: receive a plurality of numbers from the non-linear function; and apply scalar quantizers to the received numbers.
 2. The method of claim 1, wherein the compression function comprises an alternating sequence of a first type of functions and a second type of functions, wherein at least one of the first type of functions is based on parameters from the first set of parameters, and wherein the second type of functions are non-linear functions.
 3. The method of claim 2, wherein: an order of the functions in the alternating sequence of the first type of functions and the second type of functions is predefined; the first type of functions are linear functions or are functions comprising a linear portion and a constant portion; and the second type of functions are predefined, wherein the quantizer is configured to: receive a plurality of numbers; and apply scalar quantizers to the received numbers.
 4. The method of claim 1, further comprising: receiving a second set of parameters, the second set of parameters indicating a decompression function for decompressing downlink channel estimates which have been compressed using the compression function; determining, based on the first set of parameters and the second set of parameters, an updated value for at least one parameter from the first set of parameters; forming an updated compression function based on the updated value; compressing downlink channel estimates using the updated compression function; and transmitting the downlink channel estimates compressed using the updated compression function.
 5. The method of claim 4, further comprising: receiving a third set of one or more parameters, the third set of one or more parameters indicating an objective function for evaluating performance of the compression function, wherein the updated value for at least one parameter from the first set of parameters is determined using the objective function, the method further comprising: determining, based on the first set of parameters and the second set of parameters, an updated value for at least one parameter from the second set of parameters; and transmitting the updated value for at least one parameter from the second set of parameters.
 6. The method of claim 1, wherein the downlink channel estimates comprise information about channels from antenna ports of the network node to antenna ports of the UE, the method further comprising: determining the downlink channel estimates using downlink reference signals.
 7. A method of operating a network node, the method comprising: determining a first set of parameters, the first set of parameters indicating a compression function for compressing downlink channel estimates at a user equipment (UE); determining a decompression function for decompressing downlink channel estimates which have been compressed by the UE using the compression function; transmitting the first set of parameters; receiving compressed downlink channel estimates; and decompressing the compressed downlink channel estimates using the decompression function, wherein the decompression function comprises a neural network having: a linear function; and a non-linear function, wherein determining the decompression function comprises: determining the linear function, wherein the first function is configured to: receive input data and provide output data in a higher dimensional space than the input data, wherein the non-linear function is configured to apply a non-linear activation function to each of a plurality of numbers in the output data of the linear function.
 8. The method of claim 7, wherein: the second function is predefined.
 9. The method of claim 7, wherein the decompression function comprises an alternating sequence of a first type of functions and a second type of functions, wherein the second type of functions are non-linear functions, and wherein determining the decompression function comprises: determining at least one of the first type of functions.
 10. The method of claim 9, wherein: an order of the functions in the alternating sequence of the first type of functions and the second type of functions is predefined; the first type of functions are linear functions or are functions comprising a linear portion and a constant portion; and the second type of functions are predefined.
 11. The method of claim 7, wherein the first set of parameters and the decompression function are determined based on: information about the terminal device; and/or information about the network node; and/or information about a cell of the network node.
 12. The method of claim 7, wherein the first set of parameters and the decompression function are determined based on at least one of: a position of the network node; a position of the terminal device; an expected pathloss for the terminal device; a precoding method used by the network node; a type of preceding used to transmit downlink reference signals; or a time and/or frequency granularity of channel state information (CSI) related measurements and reporting.
 13. The method of claim 7, wherein determining the decompression function comprises: determining a second set of parameters; and forming the decompression function based on the second set of parameters, wherein the first set of parameters and the second set of parameters are determined by at least: evaluating performance of different compression functions and decompression functions using an objective function; and selecting values for the first set of parameter and the second set of parameters based on the evaluation, wherein the evaluation is performed using one or more neural networks, and wherein the first set of parameters and the second set of parameters correspond to weights in the one or more neural networks.
 14. A user equipment (UE) comprising: processing circuitry; and at least one memory, the at least one memory containing instructions that when executed by the processing circuitry cause the UE to: receive a first set of parameters from a network node; form a compression function based on the first set of parameters; compress downlink channel estimates using the compression function; and transmit the compressed downlink channel estimates, wherein the compression function comprises a neural network having: a linear function; a non-linear function; and a quantizer, wherein the linear function is formed based on at least one of the parameters from the first set of parameters, wherein the linear function is configured to: receive input data; and provide output data with a lower dimension than the input data, wherein the non-linear function is configured to apply a non-linear activation function to each of a plurality of numbers in the output data of the linear function, wherein the quantizer is configured to: receive a plurality of numbers from the non-linear function; and apply scalar quantizers to the received numbers.
 15. A network node comprising: processing circuitry; and at least one memory, the at least one memory containing instructions that when by the processing circuitry cause the network node to: determine a first set of parameters, the first set of parameters indicating a compression function for compressing downlink channel estimates at a user equipment (UE); determine a decompression function for decompressing downlink channel estimates which have been compressed by the UE using the compression function; transmit the first set of parameters; receive compressed downlink channel estimates; and decompress the compressed downlink channel estimates using the decompression function, wherein the decompression function comprises a neural network having: a linear function; and a non-linear function, wherein determining the decompression function comprises: determining the linear function, wherein the linear function is configured to: receive input data and provide output data in a higher dimensional space than the input data, wherein the non-linear function is configured to apply a non-linear activation function to each of a plurality of numbers in the output data of the linear function. 